survey

A Survey and an Empirical Evaluation of Multi-View Clustering Approaches

Authors:

Jingwei DuAuthors Info & Claims

ACM Computing Surveys, Volume 56, Issue 7

Article No.: 187, Pages 1 - 38

https://doi.org/10.1145/3645108

Published: 09 April 2024 Publication History

Abstract

Multi-view clustering (MVC) holds a significant role in domains like machine learning, data mining, and pattern recognition. Despite the development of numerous new MVC approaches employing various techniques, there remains a gap in comprehensive studies evaluating the characteristics and performance of these approaches. This gap hinders the in-depth understanding and rational utilization of the recently developed MVC techniques. This study formalizes the basic concepts of MVC and analyzes their techniques. It then introduces a novel taxonomy for MVC approaches and presents the working mechanisms and characteristics of representative MVC approaches developed in recent years. Moreover, it summarizes representative datasets and performance metrics commonly employed for evaluating MVC approaches. Furthermore, we have meticulously chosen 35 representative MVC approaches to conduct an empirical evaluation across seven real-world benchmark datasets, offering valuable insights into the realm of MVC approaches.

Supplementary Material

3645108-supp (3645108-supp.pdf)

Supplementary material

Download
1.09 MB

References

[1]

Zhao Kang, Xinjia Zhao, Chong Peng, Hongyuan Zhu, Joey Tianyi Zhou, Xi Peng, Wenyu Chen, and Zenglin Xu. 2020. Partition level multiview subspace clustering. Neural Networks 122 (2020), 279–288.

Digital Library

[2]

Junwei Han, Jinglin Xu, Feiping Nie, and Xuelong Li. 2022. Multi-view k-means clustering with adaptive sparse memberships and weight allocation. IEEE Transactions on Knowledge and Data Engineering 34, 2 (2022), 816–827.

Digital Library

[3]

Yan Yang and Hao Wang. 2018. Multi-view clustering: A survey. Big Data Mining and Analytics 1, 2 (2018), 83–107.

[4]

Chang Xu, Dacheng Tao, and Chao Xu. 2013. A survey on multi-view learning. arXiv preprint arXiv:1304.5634 (2013).

[5]

Zhaoliang Chen, Lele Fu, Jie Yao, Wenzhong Guo, Claudia Plant, and Shiping Wang. 2023. Learnable graph convolutional network and feature fusion for multi-view learning. Information Fusion 95 (2023), 109–119.

Digital Library

[6]

Lele Fu, Pengfei Lin, Athanasios V. Vasilakos, and Shiping Wang. 2020. An overview of recent multi-view clustering. Neurocomputing 402 (2020), 148–161.

[7]

Shiliang Sun. 2013. A survey of multi-view machine learning. Neural Computing and Applications 23, 7 (2013), 2031–2038.

[8]

Jing Zhao, Xijiong Xie, Xin Xu, and Shiliang Sun. 2017. Multi-view learning overview: Recent progress and new challenges. Information Fusion 38 (2017), 43–54.

Digital Library

[9]

Yingming Li, Ming Yang, and Zhongfei Zhang. 2018. A survey of multi-view representation learning. IEEE Transactions on Knowledge and Data Engineering 31, 10 (2018), 1863–1883.

[10]

Shu Li, Wen-Tao Li, and Wei Wang. 2020. Co-GCN for multi-view semi-supervised learning. In Proceedings of the AAAI Conference on Artificial Intelligence. 4691–4698.

[11]

Guoqing Chao, Shiliang Sun, and Jinbo Bi. 2017. A survey on multi-view clustering. arXiv preprint arXiv:1712.06246 (2017).

[12]

Sanjay Kumar Anand and Suresh Kumar. 2022. Experimental comparisons of clustering approaches for data representation. ACM Computing Surveys 55, 3 (2022), 1–33.

Digital Library

[13]

Han Xiao, Yidong Chen, and Xiaodong Shi. 2019. Knowledge graph embedding based on multi-view clustering framework. IEEE Transactions on Knowledge and Data Engineering 33, 2 (2019), 585–596.

Digital Library

[14]

Syed Fawad Hussain, Khadija Khan, and Rashad Jillani. 2022. Weighted multi-view co-clustering (WMVCC) for sparse data. Applied Intelligence 52, 1 (2022), 398–416.

Digital Library

[15]

Abhishek Kumar, Piyush Rai, and Hal Daume. 2011. Co-regularized multi-view spectral clustering. In Proceedings of the 24th International Conference on Neural Information Processing Systems (NIPS ’11). 1413–1421.

Digital Library

[16]

Jialu Liu, Chi Wang, Jing Gao, and Jiawei Han. 2013. Multi-view clustering via joint nonnegative matrix factorization. In Proceedings of the 2013 SIAM International Conference on Data Mining. 252–260.

[17]

Yeqing Li, Feiping Nie, Heng Huang, and Junzhou Huang. 2015. Large-scale multi-view spectral clustering via bipartite graph. In Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2750–2756.

[18]

Lusi Li and Haibo He. 2022. Bipartite graph based multi-view clustering. IEEE Transactions on Knowledge and Data Engineering 34, 7 (2022), 3111–3125.

[19]

Jie Wen, Zheng Zhang, Lunke Fei, Bob Zhang, Yong Xu, Zhao Zhang, and Jinxing Li. 2022. A survey on incomplete multiview clustering. IEEE Transactions on Systems, Man, and Cybernetics: Systems 53, 2 (2022), 1136–1149.

[20]

Yanying Mei, Zhenwen Ren, Bin Wu, Yanhua Shao, and Tao Yang. 2022. Robust graph-based multi-view clustering in latent embedding space. International Journal of Machine Learning and Cybernetics 13, 2 (2022), 497–508.

[21]

Ben Yang, Xuetao Zhang, Badong Chen, Feiping Nie, Zhiping Lin, and Zhixiong Nan. 2022. Efficient correntropy-based multi-view clustering with anchor graph embedding. Neural Networks 146 (2022), 290–302.

Digital Library

[22]

Shiliang Sun, Feng Jin, and Wenting Tu. 2011. View construction for multi-view semi-supervised learning. In Advances in Neural Networks—ISNN 2011. Lecture Notes in Computer Science, Vol. 6675. Springer, 595–601.

Digital Library

[23]

Cam-Tu Nguyen, De-Chuan Zhan, and Zhi-Hua Zhou. 2013. Multi-modal image annotation with multi-instance multi-label LDA. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence. 1558–1564.

Digital Library

[24]

Jianguo Zhao, Gengyu Lyu, and Songhe Feng. 2022. Linear neighborhood reconstruction constrained latent subspace discovery for incomplete multi-view clustering. Applied Intelligence 52, 1 (2022), 982–993.

Digital Library

[25]

Linlin Zong, Faqiang Miao, Xianchao Zhang, Xinyue Liu, and Hong Yu. 2021. Incomplete multi-view clustering with partially mapped instances and clusters. Knowledge-Based Systems 212 (2021), 106615.

[26]

Krishna Kumar Sharma and Ayan Seal. 2021. Multi-view spectral clustering for uncertain objects. Information Sciences 547 (2021), 723–745.

[27]

Minmin Miao, Wenbin Zhang, Wenjun Hu, and Ruiqin Wang. 2020. An adaptive multi-domain feature joint optimization framework based on composite kernels and ant colony optimization for motor imagery EEG classification. Biomedical Signal Processing and Control 61 (2020), 101994.

[28]

Wei Wang and Zhi-Hua Zhou. 2007. Analyzing co-training style algorithms. In Proceedings of the European Conference on Machine Learning. 454–465.

Digital Library

[29]

Siwei Wang, Xinwang Liu, En Zhu, Chang Tang, Jiyuan Liu, Jingtao Hu, Jingyuan Xia, and Jianping Yin. 2019. Multi-view clustering via late fusion alignment maximization. In Proceedings of the 28th International Joint Conference on Artificial Intelligence. 3778–3784.

Digital Library

[30]

Feiping Nie, Jing Li, and Xuelong Li. 2017. Self-weighted multiview clustering with multiple graphs. In Proceedings of the 26th International Joint Conference on Artificial Intelligence. 2564–2570.

[31]

Tian Xia, Dacheng Tao, Tao Mei, and Yongdong Zhang. 2010. Multiview spectral embedding. IEEE Transactions on Systems, Man, and Cybernetics: Part B (Cybernetics) 40, 6 (2010), 1438–1446.

Digital Library

[32]

Feiping Nie, Jing Li, and Xuelong Li. 2016. Parameter-free auto-weighted multiple graph learning: A framework for multiview clustering and semi-supervised classification. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. 1881–1887.

[33]

Guowang Du, Lihua Zhou, Lizhen Wang, and Jingwei Du. 2022. Multi-view clustering based on two-level weights. Journal of Computer Research and Developments 59, 4 (2022), 15.

[34]

Xiaoqian Zhang, Jing Wang, Xuqian Xue, Huaijiang Sun, and Jiangmei Zhang. 2022. Confidence level auto-weighting robust multi-view subspace clustering. Neurocomputing 475 (2022), 38–52.

Digital Library

[35]

Yu-Meng Xu, Chang-Dong Wang, and Jian-Huang Lai. 2016. Weighted multi-view clustering with feature selection. Pattern Recognition 53 (2016), 25–35.

Digital Library

[36]

Jing Liu, Fuyuan Cao, Xiao-Zhi Gao, Liqin Yu, and Jiye Liang. 2020. A cluster-weighted kernel k-means method for multi-view clustering. In Proceedings of the AAAI Conference on Artificial Intelligence. 4860–4867.

[37]

Shi-Zhe Hu, Zheng-Zheng Lou, Ruo-Bin Wang, Xiao-Qiang Yan, and Yang-Dong Y. 2020. Dual-weighted multi-view clustering. Journal of Computer Science 43, 9 (2020), 1708–1720.

[38]

Zhao Kang, Guoxin Shi, Shudong Huang, Wenyu Chen, Xiaorong Pu, Joey Tianyi Zhou, and Zenglin Xu. 2020. Multi-graph fusion for multi-view spectral clustering. Knowledge-Based Systems 189 (2020), 105102.

Digital Library

[39]

George Trigeorgis, Konstantinos Bousmalis, Stefanos Zafeiriou, and Björn W. Schuller. 2016. A deep matrix factorization method for learning attribute representations. IEEE Transactions on Pattern Analysis & Machine Intelligence 39, 3 (2022), 417–429.

Digital Library

[40]

Cai Xu, Ziyu Guan, Wei Zhao, Yunfei Niu, Quan Wang, and Zhiheng Wang. 2018. Deep multi-view concept learning. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. 2898–2904.

Digital Library

[41]

Chih-Jen Lin. 2007. Projected gradient methods for nonnegative matrix factorization. Neural Computation 19, 10 (2007), 2756–2779.

Digital Library

[42]

Zhouchen Lin, Risheng Liu, and Zhixun Su. 2011. Linearized alternating direction method with adaptive penalty for low-rank representation. Advances in Neural Information Processing Systems 24 (2011), 612–620.

[43]

Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, and Jonathan Eckstein. 2011. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends® in Machine Learning 3, 1 (2011), 1–122.

Digital Library

[44]

Hao Wang, Yan Yang, and Tianrui Li. 2016. Multi-view clustering via concept factorization with local manifold regularization. In Proceedings of the 2016 IEEE 16th International Conference on Data Mining (ICDM ’16). 1245–1250.

[45]

Linlin Zong, Xianchao Zhang, Long Zhao, Hong Yu, and Qianli Zhao. 2017. Multi-view clustering via multi-manifold regularized non-negative matrix factorization. Neural Networks 88 (2017), 74–89.

[46]

Naiyao Liang, Zuyuan Yang, Zhenni Li, Shengli Xie, and Chun-Yi Su. 2020. Semi-supervised multi-view clustering with graph-regularized partially shared non-negative matrix factorization. Knowledge-Based Systems 190 (2020), 105185.

Digital Library

[47]

Hao Cai, Bo Liu, Yanshan Xiao, and Luyue Lin. 2019. Semi-supervised multi-view clustering based on constrained nonnegative matrix factorization. Knowledge-Based Systems 182 (2019), 104798.

Digital Library

[48]

Hao Cai, Bo Liu, Yanshan Xiao, and Luyue Lin. 2020. Semi-supervised multi-view clustering based on orthonormality-constrained nonnegative matrix factorization. Information Sciences 536 (2020), 171–184.

[49]

Naiyao Liang, Zuyuan Yang, Zhenni Li, Weijun Sun, and Shengli Xie. 2020. Multi-view clustering by non-negative matrix factorization with co-orthogonal constraints. Knowledge-Based Systems 194 (2020), 105582.

[50]

Xiao Zheng, Chang Tang, Xinwang Liu, and En Zhu. 2023. Multi-view clustering via matrix factorization assisted k-means. Neurocomputing 534 (2023), 45–54.

Digital Library

[51]

Peng Luo, Jinye Peng, Ziyu Guan, and Jianping Fan. 2018. Dual regularized multi-view non-negative matrix factorization for clustering. Neurocomputing 294 (2018), 1–11.

[52]

Mingyang Liu, Zuyuan Yang, Lingjiang Li, Zhenni Li, and Shengli Xie. 2023. Auto-weighted collective matrix factorization with graph dual regularization for multi-view clustering. Knowledge-Based Systems 260 (2023), 110145.

Digital Library

[53]

Lihua Zhou, Guowang Du, Kevin Lü, and Lizhen Wang. 2021. A network-based sparse and multi-manifold regularized multiple non-negative matrix factorization for multi-view clustering. Expert Systems with Applications 174 (2021), 114783.

Digital Library

[54]

Ben Yang, Xuetao Zhang, Feiping Nie, Fei Wang, Weizhong Yu, and Rong Wang. 2020. Fast multi-view clustering via nonnegative and orthogonal factorization. IEEE Transactions on Image Processing 30 (2020), 2575–2586.

[55]

Khamis Houfar, Djamel Samai, Fadi Dornaika, Azeddine Benlamoudi, Khaled Bensid, and Abdelmalik Taleb-Ahmed. 2023. Automatically weighted binary multi-view clustering via deep initialization (AW-BMVC). Pattern Recognition 137 (2023), 109281.

Digital Library

[56]

Zheng Zhang, Li Liu, Fumin Shen, Heng Tao Shen, and Ling Shao. 2019. Binary multi-view clustering. IEEE Transactions on Pattern Analysis & Machine Intelligence 41, 7 (2019), 1774–1782.

[57]

Jianxi Zhao, Fangyuan Kang, Qingrong Zou, and Xiaonan Wang. 2023. Multi-view clustering with orthogonal mapping and binary graph. Expert Systems with Applications 213 (2023), 118911.

Digital Library

[58]

Shuai Chang, Jie Hu, Tianrui Li, Hao Wang and Bo Peng. 2021. Multi-view clustering via deep concept factorization. Knowledge-Based Systems 217 (2021), 106807.

Digital Library

[59]

Chen Zhang, Siwei Wang, Jiyuan Liu, Sihang Zhou, Pei Zhang, Xinwang Liu, En Zhu, and Changwang Zhang. 2021. Multi-view clustering via deep matrix factorization and partition alignment. In Proceedings of the 29th ACM International Conference on Multimedia. 4156–4164.

Digital Library

[60]

Jianqiang Li, Guoxu Zhou, Yuning Qiu, Yanjiao Wang, Yu Zhang, and Shengli Xie. 2020. Deep graph regularized non-negative matrix factorization for multi-view clustering. Neurocomputing 390 (2020), 108–116.

[61]

Guowang Du, Lihua Zhou, Kevin Lü, and Haiyan Ding. 2021. Deep multiple non-negative matrix factorization for multi-view clustering. Intelligent Data Analysis 25, 2 (2021), 339–357.

Digital Library

[62]

Zexi Chen, Pengfei Lin, Zhaoliang Chen, Dongyi Ye, and Shiping Wang. 2022. Diversity embedding deep matrix factorization for multi-view clustering. Information Sciences 610 (2022), 114–125.

Digital Library

[63]

Handong Zhao, Zhengming Ding, and Yun Fu. 2017. Multi-view clustering via deep matrix factorization. In Proceedings of the 2017 31th AAAI Conference on Artificial Intelligence (AAAI ’17). 2921–2927.

[64]

Zhenwen Ren, Haoyun Lei, Quansen Sun, and Chao Yang. 2020. Simultaneous learning coefficient matrix and affinity graph for multiple kernel clustering. Information Sciences 547 (2020), 289–306.

[65]

Zhenwen Ren, Haoran Li, Chao Yang, and Quansen Sun. 2020. Multiple kernel subspace clustering with local structural graph and low-rank consensus kernel learning. Knowledge-Based Systems 188 (2020), 105040.

Digital Library

[66]

Shudong Huang, Zhao Kang, Ivor W. Tsang, and Zenglin Xu. 2019. Auto-weighted multi-view clustering via kernelized graph learning. Pattern Recognition 88 (2019), 174–184.

Digital Library

[67]

Yongyong Chen, Xiaolin Xiao, and Yicong Zhou. 2020. Jointly learning kernel representation tensor and affinity matrix for multi-view clustering. IEEE Transactions on Multimedia 22, 8 (2020), 1985–1997.

[68]

Guang-Yu Zhang, Xiao-Wei Chen, Yu-Ren Zhou, Chang-Dong Wang, Dong Huang, and Xiao-Yu He. 2022. Kernelized multi-view subspace clustering via auto-weighted graph learning. Applied Intelligence 52, 1 (2022), 716–731.

Digital Library

[69]

Xi Peng, Zhenyu Huang, Jiancheng Lv, Hongyuan Zhu, and Joey Tianyi Zhou. 2019. COMIC: Multi-view clustering without parameter selection. In Proceedings of the International Conference on Machine Learning. 5092–5101.

[70]

Quanxue Gao, Zhizhen Wan, Ying Liang, Qianqian Wang, Yang Liu, and Ling Shao. 2020. Multi-view projected clustering with graph learning. Neural Networks 126 (2020), 335–346.

[71]

Zhenwen Ren, Xingfeng Li, Mithun Mukherjee, Yuqing Huang, Quansen Sun, and Zhen Huang. 2021. Robust multi-view graph clustering in latent energy-preserving embedding space. Information Sciences 569 (2021), 582–595.

[72]

Jian Dai, Zhenwen Ren, Yunzhi Luo, Hong Song, and Jian Yang. 2021. Multi-view clustering with latent low-rank proxy graph learning. Cognitive Computation 13, 4 (2021), 1049–1060.

[73]

Peiguang Jing, Yuting Su, Zhengnan Li, and Liqiang Nie. 2021. Learning robust affinity graph representation for multi-view clustering. Information Sciences 544 (2022), 155–167.

[74]

Hao Wang, Yan Yang, and Bing Liu. 2019. GMC: Graph-based multi-view clustering. IEEE Transactions on Knowledge and Data Engineering 32, 6 (2019), 1116–1129.

[75]

Hao Wang, Yan Yang, Bing Liu, and Hamido Fujita. 2019. A study of graph-based system for multi-view clustering. Knowledge-Based Systems 163 (2019), 1009–1019.

[76]

Huiling Xu, Xiangdong Zhang, Wei Xia, Quanxue Gao, and Xinbo Gao. 2020. Low-rank tensor constrained co-regularized multi-view spectral clustering. Neural Networks 132 (2020), 245–252.

[77]

Yujiao Zhao, Yu Yun, Xiangdong Zhang, Qin Li, and Quanxue Gao. 2022. Multi-view spectral clustering with adaptive graph learning and tensor Schatten p-norm. Neurocomputing 468 (2022), 257–264.

Digital Library

[78]

Yongyong Chen, Xiaolin Xiao, Chong Peng, Guangming Lu, and Yicong Zhou. 2022. Low-rank tensor graph learning for multi-view subspace clustering. IEEE Transactions on Circuits and Systems for Video Technology 32, 1 (2022), 92–104.

Digital Library

[79]

Sally El Hajjar, Fadi Dornaika, and Fahed Abdallah. 2022. Multi-view spectral clustering via constrained nonnegative embedding. Information Fusion 78 (2022), 209–217.

Digital Library

[80]

Shiping Wang, Shunxin Xiao, William Zhu, and Yingya Guo. 2022. Multi-view fuzzy clustering of deep random walk and sparse low-rank embedding. Information Sciences 586 (2022), 224–238.

Digital Library

[81]

Man-Sheng Chen, Ling Huang, Chang-Dong Wang, and Dong Huang. 2019. Multi-view spectral clustering via multi-view weighted consensus and matrix-decomposition based discretization. In Proceedings of the 24th International Conference on Database Systems for Advanced Applications. 175–190.

[82]

Man-Sheng Chen, Ling Huang, Chang-Dong Wang, and Dong Huang. 2020. Multi-view clustering in latent embedding space. In Proceedings of the AAAI Conference on Artificial Intelligence. 3513–3520.

[83]

Mashaan Alshammari, John Stavrakakis, and Masahiro Takatsuka. 2021. Refining a k-nearest neighbor graph for a computationally efficient spectral clustering. Pattern Recognition 114 (2021), 107869.

[84]

Ziheng Li, Zhanxuan Hu, Feiping Nie, Rong Wang, and Xuelong Li. 2022. Multi-view clustering based on generalized low rank approximation. Neurocomputing 471 (2022), 251–259.

Digital Library

[85]

Zhanxuan Hu, Feiping Nie, Rong Wang, and Xuelong Li. 2020. Multi-view spectral clustering via integrating nonnegative embedding and spectral embedding. Information Fusion 55 (2020), 251–259.

[86]

Shaojun Shi, Feiping Nie, Rong Wang, and Xuelong Li. 2020. Auto-weighted multi-view clustering via spectral embedding. Neurocomputing 399 (2020), 369–379.

[87]

Guo Zhong, Ting Shu, Guoheng Huang, and Xueming Yan. 2022. Multi-view spectral clustering by simultaneous consensus graph learning and discretization. Knowledge-Based Systems 235 (2022), 107632.

Digital Library

[88]

Rong Wang, Feiping Nie, Zhen Wang, Haojie Hu, and Xuelong Li. 2019. Parameter-free weighted multi-view projected clustering with structured graph learning. IEEE Transactions on Knowledge and Data Engineering 32, 10 (2019), 2014–2025.

[89]

Zhao Kang, Zhiping Lin, Xiaofeng Zhu, and Wenbo Xu. 2021. Structured graph learning for scalable subspace clustering: From single view to multiview. IEEE Transactions on Cybernetics 52, 9 (2021), 8976–8986.

[90]

Hong Tao, Chenping Hou, Yuhua Qian, Jubo Zhu, and Dongyun Yi. 2020. Latent complete row space recovery for multi-view subspace clustering. IEEE Transactions on Image Processing 29 (2020), 8083–8096.

Digital Library

[91]

Xiaomeng Si, Qiyue Yin, Xiaojie Zhao, and Li Yao. 2022. Consistent and diverse multi-view subspace clustering with structure constraint. Pattern Recognition 121 (2022), 108196.

Digital Library

[92]

Yalan Qin, Hanzhou Wu, Xinpeng Zhang, and Guorui Feng. 2021. Semi-supervised structured subspace learning for multi-view clustering. IEEE Transactions on Image Processing 31 (2021), 1–14.

Digital Library

[93]

Xiaolan Liu, Gan Pan, and Mengying Xie. 2021. Multi-view subspace clustering with adaptive locally consistent graph regularization. Neural Computing and Applications 33, 22 (2021), 15397–15412.

Digital Library

[94]

Xiaobo Wang, Zhen Lei, Xiaojie Guo, Changqing Zhang, Hailin Shi, and Stan Z. Li. 2019. Multi-view subspace clustering with intactness-aware similarity. Pattern Recognition 88 (2019), 50–63.

Digital Library

[95]

Zhiyong Yang, Qianqian Xu, Weigang Zhang, Xiaochun Cao, and Qingming Huang. 2019. Split multiplicative multi-view subspace clustering. IEEE Transactions on Image Processing 28, 10 (2022), 5147–5160.

Digital Library

[96]

Xiaosha Cai, Dong Huang, Guang-Yu Zhang, and Chang-Dong Wang. 2023. Seeking commonness and inconsistencies: A jointly smoothed approach to multi-view subspace clustering. Information Fusion 91 (2023), 364–375.

Digital Library

[97]

Tao Zhou, Changqing Zhang, Xi Peng, Harish Bhaskar, and Jie Yang. 2019. Dual shared-specific multiview subspace clustering. IEEE Transactions on Cybernetics 50, 8 (2019), 3517–3530.

[98]

Rong-Wei Cao, Ji-Hua Zhu, Wen-Yu Hao, Chang-Qing Zhang, Zhuo-Han Zhang, and Zhong-Yu Li. 2022. Dual weighted multi-view subspace clustering. Journal of Software 33, 2 (2022), 585–597.

[99]

Run-Kun Lu, Jian-Wei Liu, and Xin Zuo. 2021. Attentive multi-view deep subspace clustering net. Neurocomputing 435 (2021), 186–196.

[100]

Xiaoqian Zhang, Huaijiang Sun, Zhigui Liu, Zhenwen Ren, Qiongjie Cui, and Yanmeng Li. 2019. Robust low-rank kernel multi-view subspace clustering based on the schatten p-norm and correntropy. Information Sciences 477 (2019), 430–447.

[101]

Xiaoqian Zhang, Zhenwen Ren, Huaijiang Sun, Keqiang Bai, Xinghua Feng, and Zhigui Liu. 2021. Multiple kernel low-rank representation-based robust multi-view subspace clustering. Information Sciences 551 (2021), 324–340.

[102]

Guang-Yu Zhang, Yu-Ren Zhou, Xiao-Yu He, Chang-Dong Wang, and Dong Huang. 2020. One-step kernel multi-view subspace clustering. Knowledge-Based Systems 189 (2020), 105126.

Digital Library

[103]

Lele Fu, Zhaoliang Chen, Yongyong Chen, and Shiping Wang. 2023. Unified low-rank tensor learning and spectral embedding for multi-view subspace clustering. IEEE Transactions on Multimedia 25 (2023), 4972–4985.

Digital Library

[104]

Haiyan Wang, Guoqiang Han, Junyu Li, Bin Zhang, Jiazhou Chen, Yu Hu, Chu Han, and Hongmin Cai. 2021. Learning task-driving affinity matrix for accurate multi-view clustering through tensor subspace learning. Information Sciences 563 (2021), 290–308.

[105]

Wentao Rong, Enhong Zhuo, Hong Peng, Jiazhou Chen, Haiyan Wang, Chu Han, and Hongmin Cai. 2021. Learning a consensus affinity matrix for multi-view clustering via subspaces merging on Grassmann manifold. Information Sciences 547 (2021), 68–87.

[106]

Yongyong Chen, Shuqin Wang, Xiaolin Xiao, Youfa Liu, Zhongyun Hua, and Yicong Zhou. 2021. Self-paced enhanced low-rank tensor kernelized multi-view subspace clustering. IEEE Transactions on Multimedia 24 (2021), 4054–4066.

[107]

Ruihuang Li, Changqing Zhang, Qinghua Hu, Pengfei Zhu and Zheng Wang. 2019. Flexible multi-view representation learning for subspace clustering. In Proceedings of the 28th International Joint Conference on Artificial Intelligence. 2916–2922.

Digital Library

[108]

Zhe Xue, Junping Du, Dawei Du, and Siwei Lyu. 2019. Deep low-rank subspace ensemble for multi-view clustering. Information Sciences 482 (2019), 210–227.

Digital Library

[109]

Pengfei Zhu, Binyuan Hui, Changqing Zhang, Dawei Du, Longyin Wen, and Qinghua Hu. 2019. Multi-view deep subspace clustering networks. arXiv preprint arXiv:1908.01978 (2019).

[110]

Jing-Hua Yang, Chuan Chen, Hong-Ning Dai, Meng Ding, Le-Le Fu, and Zibin Zheng. 2022. Hierarchical representation for multi-view clustering: From intra-sample to intra-view to inter-view. In Proceedings of the 31st ACM International Conference on Information and Knowledge Management. 2362–2371.

Digital Library

[111]

Qianqian Wang, Jiafeng Cheng, Quanxue Gao, Guoshuai Zhao, and Licheng Jiao. 2020. Deep multi-view subspace clustering with unified and discriminative learning. IEEE Transactions on Multimedia 23 (2020), 3483–3493.

Digital Library

[112]

Xiaomeng Si, Qiyue Yin, Xiaojie Zhao, and Li Yao. 2022. Robust deep multi-view subspace clustering networks with a correntropy-induced metric. Applied Intelligence 52, 13 (2022), 14871–14887.

Digital Library

[113]

Qinghai Zheng, Jihua Zhu, Yuanyuan Ma, Zhongyu Li, and Zhiqiang Tian. 2021. Multi-view subspace clustering networks with local and global graph information. Neurocomputing 449 (2021), 15–23.

[114]

Guowang Du, Lihua Zhou, Kevin Lü, Hao Wu, and Zhimin Xu. 2023. Multiview subspace clustering with multilevel representations and adversarial regularization. IEEE Transactions on Neural Networks and Learning Systems 34, 12 (2023), 10279–10293.

[115]

Siwei Wang, Xinwang Liu, Xinzhong Zhu, Pei Zhang, Yi Zhang, Feng Gao, and En Zhu. 2021. Fast parameter-free multi-view subspace clustering with consensus anchor guidance. IEEE Transactions on Image Processing 31 (2021), 556–568.

Digital Library

[116]

Zhaoyang Li, Qianqian Wang, Zhiqiang Tao, Quanxue Gao, and Zhaohua Yang. 2019. Deep adversarial multi-view clustering network. In Proceedings of the 28th International Joint Conference on Artificial Intelligence. 2952–2958.

[117]

Guowang Du, Lihua Zhou, Yudi Yang, Kevin Lü, and Lizhen Wang. 2021. Deep multiple auto-encoder-based multi-view clustering. Data Science and Engineering 6, 3 (2021), 323–338.

[118]

Jie Xu, Yazhou Ren, Guofeng Li, Lili Pan, Ce Zhu, and Zenglin Xu. 2021. Deep embedded multi-view clustering with collaborative training. Information Sciences 573 (2021), 279–290.

[119]

Ru Wang, Lin Li, Xiaohui Tao, Xiao Dong, Peipei Wang, and Peiyu Liu. 2021. Trio-based collaborative multi-view graph clustering with multiple constraints. Information Processing & Management 58, 3 (2021), 102466.

Digital Library

[120]

Yuan Xie, Bingqian Lin, Yanyun Qu, Cuihua Li, Wensheng Zhang, Lizhuang Ma, Yonggang Wen, and Dacheng Tao. 2020. Joint deep multi-view learning for image clustering. IEEE Transactions on Knowledge and Data Engineering 33, 11 (2020), 3594–3606.

Digital Library

[121]

Wei Xia, Sen Wang, Ming Yang, Quanxue Gao, Jungong Han, and Xinbo Gao. 2022. Multi-view graph embedding clustering network: Joint self-supervision and block diagonal representation. Neural Networks 145 (2022), 1–9.

Digital Library

[122]

Shunxin Xiao, Shide Du, Zhaoliang Chen, Yunhe Zhang, and Shiping Wang. 2023. Dual fusion-propagation graph neural network for multi-view clustering. IEEE Transactions on Multimedia 25 (2023), 9203–9215. DOI:

Digital Library

[123]

Shiping Wang, Zhaoliang Chen, Shide Du, and Zhouchen Lin. 2021. Learning deep sparse regularizers with applications to multi-view clustering and semi-supervised classification. IEEE Transactions on Pattern Analysis & Machine Intelligence 44, 9 (2021), 5042–5055.

[124]

Runwu Zhou and Yi-Dong Shen. 2020. End-to-end adversarial-attention network for multi-modal clustering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 14619–14628.

[125]

Zhenyu Huang, Peng Hu, Joey Tianyi Zhou, Jiancheng Lv, and Xi Peng. 2020. Partially view-aligned clustering. Advances in Neural Information Processing Systems 33 (2020), 2892–2902.

[126]

Zihan Fang, Shide Du, Xincan Lin, Jinbin Yang, Shiping Wang, and Yiqing Shi. 2023. DBO-Net: Differentiable bi-level optimization network for multi-view clustering. Information Sciences 626 (2023), 572–585.

Digital Library

[127]

Daniel J. Trosten, Sigurd Lokse, Robert Jenssen, and Michael Kampffmeyer. 2021. Reconsidering representation alignment for multi-view clustering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1255–1265.

[128]

Uno Fang, Jianxin Li, Xuequan Lu, Ajmal Mian, and Zhaoquan Gu. 2023. Robust image clustering via context-aware contrastive graph learning. Pattern Recognition 138 (2023), 109340.

Digital Library

[129]

Guowang Du, Lihua Zhou, Zhongxue Li, Lizhen Wang, and Kevin Lü. 2023. Neighbor-aware deep multi-view clustering via graph convolutional network. Information Fusion 93 (2023), 330–343.

Digital Library

[130]

Fangfei Lin, Bing Bai, Kun Bai, Yazhou Ren, Peng Zhao, and Zenglin Xu. 2022. Contrastive multi-view hyperbolic hierarchical clustering. arXiv preprint arXiv:2205.02618 (2022).

[131]

Jie Xu, Huayi Tang, Yazhou Ren, Liang Peng, Xiaofeng Zhu, and Lifang He. 2022. Multi-level feature learning for contrastive multi-view clustering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 16030–16039.

[132]

Dunqiang Liu, Shu-Juan Peng, Xin Liu, Lei Zhu, Zhen Cui. and Taihao Li. 2022. Inconsistency distillation for consistency: Enhancing multi-view clustering via mutual contrastive teacher-student leaning. In Proceedings of the 2022 IEEE International Conference on Data Mining (ICDM ’22). 251–258.

[133]

Xiaozhi Deng, Dong Huang, Ding-Hua Chen, Chang-Dong Wang, and Jian-Huang Lai. 2023. Strongly augmented contrastive clustering. Pattern Recognition 139 (2023), 109470.

Digital Library

[134]

Ru Wang, Lin Li, Xiaohui Tao, Peipei Wang, and Peiyu Liu. 2022. Contrastive and attentive graph learning for multi-view clustering. Information Processing & Management 59, 4 (2022), 102967.

Digital Library

[135]

Erlin Pan and Zhao Kang. 2021. Multi-view contrastive graph clustering. Advances in Neural Information Processing Systems 34 (2021), 2148–2159.

[136]

Shiping Wang, Xincan Lin, Zihan Fang, Shide Du, and Guobao Xiao. 2022. Contrastive consensus graph learning for multi-view clustering. IEEE/CAA Journal of Automatica Sinica 9, 11 (2022), 2027–2030.

[137]

Daniel J. Trosten, Sigurd Løkse, Robert Jenssen, and Michael C. Kampffmeyer. 2023. On the effects of self-supervision and contrastive alignment in deep multi-view clustering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 23976–23985.

[138]

Abhishek Kumar and Hal Daumé. 2011. A co-training approach for multi-view spectral clustering. In Proceedings of the 28th International Conference on Machine Learning. 393–400.

[139]

Avrim Blum and Tom Mitchell. 1998. Combining labeled and unlabeled data with co-training. In Proceedings of the 11th Annual Conference on Computational Learning Theory. 92–100.

Digital Library

[140]

Zhaohong Deng, Ruixiu Liu, Peng Xu, Kup-Sze Choi, Wei Zhang, Xiaobin Tian, Te Zhang, Ling Liang, Bin Qin, and Shitong Wang. 2023. Multi-view clustering with the cooperation of visible and hidden views. IEEE Transactions on Knowledge and Data Engineering 34, 2 (2023), 803–815.

Digital Library

[141]

Sayantan Mitra and Sriparna Saha. 2021. A multi-task multi-view based multi-objective clustering algorithm. In Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR ’21). 4720–4727.

[142]

Xianchao Zhang, Xiaotong Zhang, and Han Liu. 2015. Multi-task multi-view clustering for non-negative data. In Proceedings of the 24th International Joint Conference on Artificial Intelligence. 4055–4061.

Digital Library

[143]

Xiaotong Zhang, Xianchao Zhang, Han Liu, and Xinyue Liu. 2016. Multi-task multi-view clustering. IEEE Transactions on Knowledge and Data Engineering 28, 12 (2016), 3324–3338.

Digital Library

[144]

Zhe Xue, Guorong Li, Shuhui Wang, Jun Huang, Weigang Zhang, and Qingming Huang. 2019. Beyond global fusion: A group-aware fusion approach for multi-view image clustering. Information Sciences 493 (2019), 176–191.

Digital Library

[145]

Shudong Huang, Zenglin Xu, Ivor W. Tsang, and Zhao Kang. 2020. Auto-weighted multi-view co-clustering with bipartite graphs. Information Sciences 512 (2020), 18–30.

Digital Library

[146]

Shizhe Hu, Xiaoqiang Yan, and Yangdong Ye. 2020. Dynamic auto-weighted multi-view co-clustering. Pattern Recognition 99 (2020), 107101.

Digital Library

[147]

Ling Zhao, Yunpeng Ma, Shanxiong Chen, and Jun Zhou. 2022. Multi-view co-clustering with multi-similarity. Applied Intelligence 53 (2022), 16961–16972.

Digital Library

[148]

Feiping Nie, Shaojun Shi, and Xuelong Li. 2020. Auto-weighted multi-view co-clustering via fast matrix factorization. Pattern Recognition 102 (2020), 107207.

Digital Library

[149]

Shide Du, Zhanghui Liu, Zhaoliang Chen, Wenyuan Yang, and Shiping Wang. 2021. Differentiable bi-sparse multi-view co-clustering. IEEE Transactions on Signal Processing 69 (2021), 4623–4636.

[150]

Yazhou Ren, Shudong Huang, Peng Zhao, Minghao Han, and Zenglin Xu. 2020. Self-paced and auto-weighted multi-view clustering. Neurocomputing 383 (2020), 248–256.

Digital Library

[151]

Zongmo Huang, Yazhou Ren, Xiaorong Pu, Lili Pan, Dezhong Yao, and Guoxian Yu. 2021. Dual self-paced multi-view clustering. Neural Networks 140 (2021), 184–192.

[152]

Hong Tao, Chenping Hou, Xinwang Liu, Tongliang Liu, Dongyun Yi, and Jubo Zhu. 2018. Reliable multi-view clustering. In Proceedings of the AAAI Conference on Artificial Intelligence. 4123-4130.

[153]

Changan Yuan, Yonghua Zhu, Zhi Zhong, Wei Zheng, and Xiaofeng Zhu. 2022. Robust self-tuning multi-view clustering. World Wide Web 25, 2 (2022), 489–512.

Digital Library

[154]

Menglei Hu and Songcan Chen. 2018. Doubly aligned incomplete multi-view clustering. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. 2262–2268.

Digital Library

[155]

Mouxing Yang, Yunfan Li, Peng Hu, Jinfeng Bai, Jiancheng Lv, and Xi Peng. 2022. Robust multi-view clustering with incomplete information. IEEE Transactions on Pattern Analysis & Machine Intelligence 45, 1 (2022), 1055–1069.

[156]

Liang Zhao, Jie Zhang, Qiuhao Wang, and Zhikui Chen. 2021. Dual alignment self-supervised incomplete multi-view subspace clustering network. IEEE Signal Processing Letters 28 (2021), 2122–2126.

[157]

Jiaqi Jin, Siwei Wang, Zhibin Dong, Xinwang Liu, and En Zhu. 2023. Deep incomplete multi-view clustering with cross-view partial sample and prototype alignment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 11600–11609.

[158]

Guoqing Chao, Songtao Wang, Shiming Yang, Chunshan Li, and Dianhui Chu. 2022. Incomplete multi-view clustering with multiple imputation and ensemble clustering. Applied Intelligence 52, 13 (2022), 14811–14821.

Digital Library

[159]

Jie Xu, Chao Li, Yazhou Ren, Liang Peng, Yujie Mo, Xiaoshuang Shi, and Xiaofeng Zhu. 2022. Deep incomplete multi-view clustering via mining cluster complementarity. In Proceedings of the AAAI Conference on Artificial Intelligence. 8761–8769.

[160]

Guoqing Chao, Jiangwen Sun, Jin Lu, An-Li Wang, Daniel D. Langleben, Chiang-Shan Li, and Jinbo Bi. 2019. Multi-view cluster analysis with incomplete data to understand treatment effects. Information Sciences 494 (2019), 278–293.

Digital Library

[161]

Jie Wen, Zheng Zhang, Zhao Zhang, Lunke Fei, and Meng Wang. 2020. Generalized incomplete multiview clustering with flexible locality structure diffusion. IEEE Transactions on Cybernetics 51, 1 (2020), 101–114.

[162]

Naiyao Liang, Zuyuan Yang, Lingjiang Li, Zhenni Li, and Shengli Xie. 2021. Incomplete multiview clustering with cross-view feature transformation. IEEE Transactions on Artificial Intelligence 3, 5 (2021), 749–762.

[163]

Naiyao Liang, Zuyuan Yang, Zhenni Li, and Wei Han. 2022. Incomplete multi-view clustering with incomplete graph-regularized orthogonal non-negative matrix factorization. Applied Intelligence 52, 13 (2022), 14607–14623.

Digital Library

[164]

Xiangyu Liu and Peng Song. 2022. Incomplete multi-view clustering via virtual-label guided matrix factorization. Expert Systems with Applications 210 (2022), 118408.

Digital Library

[165]

Zhenjiao Liu, Zhikui Chen, Yue Li, Liang Zhao, Tao Yang, Reza Farahbakhsh, Noel Crespi, and Xiaodi Huang. 2023. IMC-NLT: Incomplete multi-view clustering by NMF and low-rank tensor. Expert Systems with Applications 221 (2023), 119742.

Digital Library

[166]

Wei Zhou, Hao Wang, and Yan Yang. 2019. Consensus graph learning for incomplete multi-view clustering. In Proceedings of the 23rd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining. 529–540.

Digital Library

[167]

Jianlun Liu, Shaohua Teng, Lunke Fei, Wei Zhang, Xiaozhao Fang, Zhuxiu Zhang, and Naiqi Wu. 2021. A novel consensus learning approach to incomplete multi-view clustering. Pattern Recognition 115 (2021), 107890.

[168]

Jun Yin and Shiliang Sun. 2023. Incomplete multi-view clustering with reconstructed views. IEEE Transactions on Knowledge and Data Engineering 35, 3 (2023), 2671–2682.

[169]

Menglei Hu and Songcan Chen. 2019. One-pass incomplete multi-view clustering. In Proceedings of the AAAI Conference on Artificial Intelligence. 3838–3845.

Digital Library

[170]

Jun Yin and Shiliang Sun. 2022. Incomplete multi-view clustering with cosine similarity. Pattern Recognition 123 (2022), 108371.

Digital Library

[171]

Xinwang Liu, Xinzhong Zhu, Miaomiao Li, Lei Wang, En Zhu, Tongliang Liu, Marius Kloft, Dinggang Shen, Jianping Yin, and Wen Gao. 2020. Multiple kernel k-means with incomplete kernels. IEEE Transactions on Pattern Analysis & Machine Intelligence 42, 5 (2020), 1191–1204.

[172]

Xinwang Liu, Miaomiao Li, Chang Tang, Jingyuan Xia, Jian Xiong, Li Liu, Marius Kloft, and En Zhu. 2020. Efficient and effective regularized incomplete multi-view clustering. IEEE Transactions on Pattern Analysis & Machine Intelligence 43, 8 (2020), 2634–2646.

[173]

Xingfeng Li, Quansen Sun, Zhenwen Ren, and Yinghui Sun. 2022. Dynamic incomplete multi-view imputing and clustering. In Proceedings of the 30th ACM International Conference on Multimedia. 3412–3420.

Digital Library

[174]

Dongxue Xia, Yan Yang, Shuhong Yang, and Tianrui Li. 2023. Incomplete multi-view clustering via kernelized graph learning. Information Sciences 625 (2023), 1–19.

Digital Library

[175]

Jie Wen, Zheng Zhang, Yong Xu, Bob Zhang, Lunke Fei, and Hong Liu. 2019. Unified embedding alignment with missing views inferring for incomplete multi-view clustering. In Proceedings of the AAAI Conference on Artificial Intelligence. 5393–5400.

Digital Library

[176]

Jing-Hua Yang, Le-Le Fu, Chuan Chen, Hong-Ning Dai, and Zibin Zheng. 2023. Cross-view graph matching for incomplete multi-view clustering. Neurocomputing 515 (2023), 79–88.

Digital Library

[177]

Jie Wen, Zheng Zhang, Zhao Zhang, Lei Zhu, Lunke Fei, Bob Zhang, and Yong Xu. 2021. Unified tensor framework for incomplete multi-view clustering and missing-view inferring. In Proceedings of the AAAI Conference on Artificial Intelligence. 10273–10281.

[178]

Shuping Zhao, Lunke Fei, Jie Wen, Jigang Wu, and Bob Zhang. 2023. Intrinsic and complete structure learning based incomplete multiview clustering. IEEE Transactions on Multimedia 25 (2023), 1098–1110.

Digital Library

[179]

Hao Wang, Linlin Zong, Bing Liu, Yan Yang, and Wei Zhou. 2019. Spectral perturbation meets incomplete multi-view data. In Proceedings of the 28th International Joint Conference on Artificial Intelligence. 3677–3683.

Digital Library

[180]

Jun Yin and Jianwei Jiang. 2023. Incomplete multi-view clustering based on self-representation. Neural Processing Letters 55 (2023), 8673–8587.

Digital Library

[181]

Kaiwu Zhang, Baokai Liu, Shiqiang Du, Yao Yu, and Jinmei Song. 2023. Incomplete multi-view clustering based on low-rank representation with adaptive graph regularization. Soft Computing 27, 11 (2023), 7131–7146.

Digital Library

[182]

Xiao Zheng, Xinwang Liu, Jiajia Chen, and En Zhu. 2022. Adaptive partial graph learning and fusion for incomplete multi-view clustering. International Journal of Intelligent Systems 37, 1 (2022), 991–1009.

Digital Library

[183]

Ziyu Lv, Quanxue Gao, Xiangdong Zhang, Qin Li, and Ming Yang. 2022. View-consistency learning for incomplete multiview clustering. IEEE Transactions on Image Processing 31 (2022), 4790–4802.

[184]

Naiyao Liang, Zuyuan Yang, and Shengli Xie. 2022. Incomplete multi-view clustering with sample-level auto-weighted graph fusion. IEEE Transactions on Knowledge and Data Engineering 35, 6 (2022), 6504–6511.

[185]

Jie Wen, Ke Yan, Zheng Zhang, Yong Xu, Junqian Wang, Lunke Fei, and Bob Zhang. 2020. Adaptive graph completion based incomplete multi-view clustering. IEEE Transactions on Multimedia 23 (2020), 2493–2504.

Digital Library

[186]

Lusi Li, Zhiqiang Wan, and Haibo He. 2021. Incomplete multi-view clustering with joint partition and graph learning. IEEE Transactions on Knowledge and Data Engineering 35, 1 (2021), 589–602.

[187]

Jiye Liang, Xiaolin Liu, Liang Bai, Fuyuan Cao, and Dianhui Wang. 2022. Incomplete multi-view clustering via local and global co-regularization. Science China Information Sciences 65, 5 (2022), 152105.

[188]

Qianli Zhao, Linlin Zong, Xianchao Zhang, Xinyue Liu, and Hong Yu. 2020. Incomplete multi-view spectral clustering. Journal of Intelligent & Fuzzy Systems 38, 3 (2020), 2991–3001.

Digital Library

[189]

Jie Wen, Huijie Sun, Lunke Fei, Jinxing Li, Zheng Zhang, and Bob Zhang. 2021. Consensus guided incomplete multi-view spectral clustering. Neural Networks 133 (2021), 207–219.

[190]

Wenzhang Zhuge, Chenping Hou, Xinwang Liu, Hong Tao, and Dongyun Yi. 2019. Simultaneous representation learning and clustering for incomplete multi-view data. In Proceedings of the 28th International Joint Conference on Artificial Intelligence. 4482–4488.

Digital Library

[191]

Jie Wen, Yong Xu, and Hong Liu. 2018. Incomplete multiview spectral clustering with adaptive graph learning. IEEE Transactions on Cybernetics 50, 4 (2018), 1418–1429.

[192]

Jie Wen, Zheng Zhang, Zhao Zhang, Lei Zhu, Lunke Fei, Bob Zhang, and Yong Xu. 2021. Unified tensor framework for incomplete multi-view clustering and missing-view inferring. In Proceedings of the AAAI Conference on Artificial Intelligence. 10273–10281.

[193]

Shijie Deng, Jie Wen, Chengliang Liu, Ke Yan, Gehui Xu, and Yong Xu. 2023. Projective incomplete multi-view clustering. IEEE Transactions on Neural Networks and Learning Systems. Early Access, February 10, 2023.

[194]

Xiao Yu, Hui Liu, Yuxiu Lin, Yan Wu, and Caiming Zhang. 2022. Auto-weighted sample-level fusion with anchors for incomplete multi-view clustering. Pattern Recognition 130 (2022), 108772.

Digital Library

[195]

Senhong Wang, Jiangzhong Cao, Fangyuan Lei, Jianjian Jiang, Qingyun Dai, and Bingo Wing-Kuen Ling. 2023. Multiple kernel-based anchor graph coupled low-rank tensor learning for incomplete multi-view clustering. Applied Intelligence 53, 4 (2023), 3687–3712.

Digital Library

[196]

Jun Guo and Jiahui Ye. 2019. Anchors bring ease: An embarrassingly simple approach to partial multi-view clustering. In Proceedings of the AAAI Conference on Artificial Intelligence. 118–125.

Digital Library

[197]

Jun Yin, Runcheng Cai, and Shiliang Sun. 2022. Anchor-based incomplete multi-view spectral clustering. Neurocomputing 514 (2022), 526–538.

Digital Library

[198]

Wenjue He, Zheng Zhang, Yongyong Chen, and Jie Wen. 2023. Structured anchor-inferred graph learning for universal incomplete multi-view clustering. World Wide Web 26, 1 (2023), 375–399.

Digital Library

[199]

Xia Ji, Lei Yang, Sheng Yao, Peng Zhao, and Xuejun Li. 2023. Fast and general incomplete multi-view adaptive clustering. Cognitive Computation 15, 2 (2023), 683–693.

[200]

Zhenglai Li, Chang Tang, Xiao Zheng, Xinwang Liu, Wei Zhang, and En Zhu. 2022. High-order correlation preserved incomplete multi-view subspace clustering. IEEE Transactions on Image Processing 31 (2022), 2067–2080.

[201]

Ming Yin, Xiaohua Liu, Liuyang Wang, and Guoliang He. 2023. Learning latent embedding via weighted projection matrix alignment for incomplete multi-view clustering. Information Sciences 634 (2023), 244–258.

Digital Library

[202]

Xuemei Han, Zhenwen Ren, Chuanyun Zou, and Xiaojian You. 2022. Incomplete multi-view subspace clustering based on missing-sample recovering and structural information learning. Expert Systems with Applications 208 (2022), 118165.

Digital Library

[203]

Chao Zhang, Huaxiong Li, Caihua Chen, Xiuyi Jia, and Chunlin Chen. 2022. Low-rank tensor regularized views recovery for incomplete multiview clustering. IEEE Transactions on Neural Networks and Learning Systems. Early Access, December 30, 2022.

[204]

Yongli Hu, Cuicui Luo, Boyue Wang, Junbin Gao, Yanfeng Sun, and Baocai Yin. 2021. Complete/incomplete multi-view subspace clustering via soft block-diagonal-induced regulariser. IET Computer Vision 15, 8 (2021), 618–632.

Digital Library

[205]

Zhenglai Li, Chang Tang, Xinwang Liu, Xiao Zheng, Wei Zhang, and En Zhu. 2021. Tensor-based multi-view block-diagonal structure diffusion for clustering incomplete multi-view data. In Proceedings of the 2021 IEEE International Conference on Multimedia and Expo (ICME ’21). 1–6.

[206]

Guoli Niu, Youlong Yang, and Liqin Sun. 2021. One-step multi-view subspace clustering with incomplete views. Neurocomputing 438 (2021), 290–301.

[207]

Zhiqi Yu, Mao Ye, Siying Xiao, and Liang Tian. 2022. Learning missing instances in latent space for incomplete multi-view clustering. Knowledge-Based Systems 250 (2022), 109122.

Digital Library

[208]

Guo Zhong and Chi-Man Pun. 2023. Simultaneous Laplacian embedding and subspace clustering for incomplete multi-view data. Knowledge-Based Systems 262 (2023), 110244.

Digital Library

[209]

Jiyuan Liu, Xinwang Liu, Yi Zhang, Pei Zhang, Wenxuan Tu, Siwei Wang, Sihang Zhou, Weixuan Liang, Siqi Wang, and Yuexiang Yang. 2021. Self-representation subspace clustering for incomplete multi-view data. In Proceedings of the 29th ACM International Conference on Multimedia. 2726–2734.

Digital Library

[210]

Xiang Fang, Yuchong Hu, Pan Zhou, and Dapeng Oliver Wu. 2021. Unbalanced incomplete multi-view clustering via the scheme of view evolution: Weak views are meat; strong views do eat. IEEE Transactions on Emerging Topics in Computational Intelligence 6, 4 (2021), 913–927.

[211]

Liang Zhao, Jie Zhang, Tao Yang, and Zhikui Chen. 2022. Incomplete multi-view clustering based on weighted sparse and low rank representation. Applied Intelligence 52, 13 (2022), 14822–14838.

Digital Library

[212]

Shaowei Wei, Jun Wang, Guoxian Yu, Carlotta Domeniconi, and Xiangliang Zhang. 2020. Deep incomplete multi-view multiple clusterings. In Proceedings of the 2020 IEEE International Conference on Data Mining (ICDM ’20). 651–660.

[213]

Zhe Xue, Junping Du, Changwei Zheng, Jie Song, Wenqi Ren, and Meiyu Liang. 2021. Clustering-induced adaptive structure enhancing network for incomplete multi-view data. In Proceedings of the 30th International Joint Conference on Artificial Intelligence. 3235–3241.

[214]

Qianqian Wang, Zhengming Ding, Zhiqiang Tao, Quanxue Gao, and Yun Fu. 2021. Generative partial multi-view clustering with adaptive fusion and cycle consistency. IEEE Transactions on Image Processing 30 (2021), 1771–1783.

[215]

Jie Wen, Zheng Zhang, Zhao Zhang, Zhihao Wu, Lunke Fei, Yong Xu, and Bob Zhang. 2020. DIMC-net: Deep incomplete multi-view clustering network. In Proceedings of the 28th ACM International Conference on Multimedia. 3753–3761.

Digital Library

[216]

Jie Xu, Chao Li, Liang Peng, Yazhou Ren, Xiaoshuang Shi, Heng Tao Shen, and Xiaofeng Zhu. 2023. Adaptive feature projection with distribution alignment for deep incomplete multi-view clustering. IEEE Transactions on Image Processing 32 (2023), 1354–1366.

Digital Library

[217]

Mengying Xie, Zehui Ye, Gan Pan, and Xiaolan Liu. 2021. Incomplete multi-view subspace clustering with adaptive instance-sample mapping and deep feature fusion. Applied Intelligence 51 (2021), 5584–5597.

Digital Library

[218]

Jie Wen, Zhihao Wu, Zheng Zhang, Lunke Fei, Bob Zhang, and Yong Xu. 2021. Structural deep incomplete multi-view clustering network. In Proceedings of the 30th ACM International Conference on Information and Knowledge Management. 3538–3542.

Digital Library

[219]

Cai Xu, Ziyu Guan, Wei Zhao, Hongchang Wu, Yunfei Niu, and Beilei Ling. 2019. Adversarial incomplete multi-view clustering. In Proceedings of the 28th International Joint Conference on Artificial Intelligence. 3933–3939.

Digital Library

[220]

Yiming Wang, Dongxia Chang, Zhiqiang Fu, Jie Wen, and Yao Zhao. 2022. Incomplete multiview clustering via cross-view relation transfer. IEEE Transactions on Circuits and Systems for Video Technology 33, 1 (2022), 367–378.

[221]

Huayi Tang and Yong Liu. 2022. Deep safe incomplete multi-view clustering: Theorem and algorithm. In Proceedings of the International Conference on Machine Learning. 21090–21110.

[222]

Yijie Lin, Yuanbiao Gou, Zitao Liu, Boyun Li, Jiancheng Lv, and Xi Peng. 2021. Completer: Incomplete multi-view clustering via contrastive prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 11174–11183.

[223]

Yijie Lin, Yuanbiao Gou, Xiaotian Liu, Jinfeng Bai, Jiancheng Lv, and Xi Peng. 2022. Dual contrastive prediction for incomplete multi-view representation learning. IEEE Transactions on Pattern Analysis & Machine Intelligence 45, 4 (2022), 4447–4461.

[224]

Yiming Wang, Dongxia Chang, Zhiqiang Fu, Jie Wen, and Yao Zhao. 2022. ACTIVE: Augmentation-free graph contrastive learning for partial multi-view clustering. arXiv:2203.00186 (2022).

[225]

Zhe Xue, Junping Du, Hai Zhu, Zhongchao Guan, Yunfei Long, Yu Zang, and Meiyu Liang. 2022. Robust diversified graph contrastive network for incomplete multi-view clustering. In Proceedings of the 30th ACM International Conference on Multimedia. 3936–3944.

Digital Library

[226]

Yi Zhang, Xinwang Liu, Siwei Wang, Jiyuan Liu, Sisi Dai, and En Zhu. 2021. One-stage incomplete multi-view clustering via late fusion. In Proceedings of the 29th ACM International Conference on Multimedia. 2717–2725.

Digital Library

[227]

Wei Zhang, Zhaohong Deng, Kup-Sze Choi, and Shitong Wang. 2023. End-to-end incomplete multiview fuzzy clustering with adaptive missing view imputation and cooperative learning. IEEE Transactions on Fuzzy Systems 31, 5 (2023), 1445–1459.

Digital Library

[228]

Longqi Yang, Liangliang Zhang, and Yuhua Tang. 2021. Online binary incomplete multi-view clustering. In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases. 75–90.

Digital Library

[229]

Xiang Fang, Yuchong Hu, Pan Zhou, and Dapeng Oliver Wu. 2020. V³H: View variation and view heredity for incomplete multiview clustering. IEEE Transactions on Artificial Intelligence 1, 3 (2020), 233–247.

[230]

Krishna Kumar Sharma and Ayan Seal. 2021. Outlier-robust multi-view clustering for uncertain data. Knowledge-Based Systems 211 (2021), 106567.

[231]

Leo L. Duan. 2020. Latent simplex position model: High dimensional multi-view clustering with uncertainty quantification. Journal of Machine Learning Research 21, 1 (2020), 1411–1435.

Digital Library

[232]

Yale Chang, Junxiang Chen, Michael H. Cho, Peter J. Castaldi, Edwin K. Silverman, and Jennifer G. Dy. 2017. Multiple clustering views from multiple uncertain experts. In Proceedings of the International Conference on Machine Learning. 674–683.

[233]

Hongwei Yin, Wenjun Hu, Zhao Zhang, Jungang Lou, and Minmin Miao. 2021. Incremental multi-view spectral clustering with sparse and connected graph learning. Neural Networks 144 (2021), 260–270.

Digital Library

[234]

Peng Zhou, Yi-Dong Shen, Liang Du, Fan Ye, and Xuejun Li. 2019. Incremental multi-view spectral clustering. Knowledge-Based Systems 174 (2019), 73–86.

Digital Library

[235]

Yanyong Huang, Kejun Guo, Xiuwen Yi, Zhong Li, and Tianrui Li. 2023. Incremental unsupervised feature selection for dynamic incomplete multi-view data. Information Fusion 96 (2023), 312–327.

Digital Library

[236]

Amir Rosenfeld and John K. Tsotsos. 2018. Incremental learning through deep adaptation. IEEE Transactions on Pattern Analysis & Machine Intelligence 42, 3 (2018), 651–663.

[237]

Adil Fahad, Najlaa Alshatri, Zahir Tari, Abdullah Alamri, Ibrahim Khalil, Albert Y. Zomaya, Sebti Foufou, and Abdelaziz Bouras. 2014. A survey of clustering algorithms for big data: Taxonomy and empirical analysis. IEEE Transactions on Emerging Topics in Computing 2, 3, 267–279.

[238]

Chang-Dong Wang, Jian-Huang Lai, and S. Yu Philip. 2015. Multi-view clustering based on belief propagation. IEEE Transactions on Knowledge and Data Engineering 28, 4 (2015), 1007–1021.

Digital Library

[239]

Zhenfan Wang, Xiangwei Kong, Haiyan Fu, Ming Li, and Yujia Zhang. 2015. Feature extraction via multi-view non-negative matrix factorization with local graph regularization. In Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP ’15). 3500–3504.

Digital Library

[240]

Kun Zhan, Jinhui Shi, Jing Wang, Haibo Wang, and Yuange Xie. 2018. Adaptive structure concept factorization for multiview clustering. Neural Computation 30, 4 (2018), 1080–1103.

Digital Library

[241]

Kun Zhan, Feiping Nie, Jing Wang, and Yi Yang. 2018. Multiview consensus graph clustering. IEEE Transactions on Image Processing 28, 3 (2018), 1261–1270.

Digital Library

[242]

Rongkai Xia, Yan Pan, Lei Du, and Jian Yin. 2014. Robust multi-view spectral clustering via low-rank and sparse decomposition. In Proceedings of the AAAI Conference on Artificial Intelligence. 2149–2155.

[243]

Feiping Nie, Lai Tian, and Xuelong Li. 2018. Multiview clustering via adaptively weighted procrustes. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2022–2030.

Digital Library

[244]

Xiaochun Cao, Changqing Zhang, Huazhu Fu, Si Liu, and Hua Zhang. 2015. Diversity-induced multi-view subspace clustering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 586–594.

[245]

Xiaobo Wang, Xiaojie Guo, Zhen Lei, Changqing Zhang, and Stan Z. Li. 2017. Exclusivity-consistency regularized multi-view subspace clustering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 923–931.

[246]

Shirui Luo, Changqing Zhang, Wei Zhang, and Xiaochun Cao. 2018. Consistent and specific multi-view subspace clustering. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence. 3730–3737.

[247]

Mahdi Abavisani and Vishal M. Patel. 2018. Deep multimodal subspace clustering networks. IEEE Journal of Selected Topics in Signal Processing 12, 6 (2018), 1601–1614.

[248]

Jie Xu, Yazhou Ren, Huayi Tang, Zhimeng Yang, Lili Pan, Yang Yang, and Xiaorong Pu. 2023. Self-supervised discriminative feature learning for multi-view clustering. IEEE Transactions on Knowledge and Data Engineering 35, 7 (2023), 7470–7482.

Digital Library

[249]

Guang-Yu Zhang, Chang-Dong Wang, Dong Huang, Wei-Shi Zheng, and Yu-Ren Zhou. 2018. TW-Co-k-means: Two-level weighted collaborative k-means for multi-view clustering. Knowledge-Based Systems 150 (2018), 127–138.

Digital Library

[250]

Man-Sheng Chen, Ling Huang, Chang-Dong Wang, Dong Huang, and Jian-Huang Lai. 2021. Relaxed multi-view clustering in latent embedding space. Information Fusion 68 (2021), 8–21.

[251]

Youwei Liang, Dong Huang, Chang-Dong Wang, and Philip S. Yu. 2022. Multi-view graph learning by joint modeling of consistency and inconsistency. IEEE Transactions on Neural Networks and Learning Systems. Published Online, July 27, 2022.

Cited By

Golzari Oskouei ASamadi NTanha JBouyer AArasteh B(2025)Viewpoint‐Based Collaborative Feature‐Weighted Multi‐View Intuitionistic Fuzzy Clustering Using Neighborhood InformationNeurocomputing10.1016/j.neucom.2024.128884617(128884)Online publication date: Mar-2025
https://doi.org/10.1016/j.neucom.2024.128884
Liu ZQiu HDeveci MPedrycz WSiarry P(2025) Multi-view neutrosophic -means clustering algorithms Expert Systems with Applications10.1016/j.eswa.2024.125454260(125454)Online publication date: Jan-2025
https://doi.org/10.1016/j.eswa.2024.125454
Zhang WChen HXiao QZhou LWang L(2024)PMPRec: A Pre-training encoder based on Meta-Path for Recommendation2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650559(1-7)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10650559
Show More Cited By

Index Terms

A Survey and an Empirical Evaluation of Multi-View Clustering Approaches
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. General and reference
  1. Document types
    1. Surveys and overviews

Recommendations

Multi-view clustering via spectral partitioning and local refinement

A new multi-view clustering algorithm is proposed.The proposed MVNC algorithm uses spectral partitioning and local refinement.MVNC is compared to state-of-the-art algorithms using three real-world datasets.MVNC significantly outperforms the other ...
Multi-view clustering via latent consistency multi-graph fusion
Abstract
Multi-view clustering (MVC) algorithms are prevalent in machine learning tasks due to their superiority in mining complementary information from multi-view data. Despite their widespread use, existing MVC approaches ignore the presence of ...
Multi-view Clustering with Graph Embedding for Connectome Analysis
CIKM '17: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management

Multi-view clustering has become a widely studied problem in the area of unsupervised learning. It aims to integrate multiple views by taking advantages of the consensus and complimentary information from multiple views. Most of the existing works in ...

Comments

Information & Contributors

Information

Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 56, Issue 7

July 2024

1006 pages

EISSN:1557-7341

DOI:10.1145/3613612

Editors:
David Atienza
Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland
,
Michela Milano
University of Bologna, Italy

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 April 2024

Online AM: 08 February 2024

Accepted: 31 January 2024

Revised: 25 December 2023

Received: 13 June 2022

Published in CSUR Volume 56, Issue 7

Check for updates

Author Tags

Qualifiers

Survey

Funding Sources

National Natural Science Foundation of China
Yunnan Fundamental Research Projects
Yunnan Key Laboratory of Intelligent Systems and Computing
Blockchain and Data Security Governance Engineering Research Center of Yunnan Provincial Department of Education
University Key Laboratory of Internet of Things Technology and Application in Yunnan Province

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
1,041
Total Downloads

Downloads (Last 12 months)1,041
Downloads (Last 6 weeks)192

Reflects downloads up to 25 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Golzari Oskouei ASamadi NTanha JBouyer AArasteh B(2025)Viewpoint‐Based Collaborative Feature‐Weighted Multi‐View Intuitionistic Fuzzy Clustering Using Neighborhood InformationNeurocomputing10.1016/j.neucom.2024.128884617(128884)Online publication date: Mar-2025
https://doi.org/10.1016/j.neucom.2024.128884
Liu ZQiu HDeveci MPedrycz WSiarry P(2025) Multi-view neutrosophic -means clustering algorithms Expert Systems with Applications10.1016/j.eswa.2024.125454260(125454)Online publication date: Jan-2025
https://doi.org/10.1016/j.eswa.2024.125454
Zhang WChen HXiao QZhou LWang L(2024)PMPRec: A Pre-training encoder based on Meta-Path for Recommendation2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650559(1-7)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10650559
Yin ZZhou LWang LChen H(2024)Dual-Contrastive Multi-view Clustering Under the Guidance of Global Similarity and Pseudo-labelWeb and Big Data10.1007/978-981-97-7241-4_3(35-49)Online publication date: 31-Aug-2024
https://dl.acm.org/doi/10.1007/978-981-97-7241-4_3
Ouyang XChen HYang PWang LXiao Q(2024)Attributed Heterogeneous Graph Embedding with Meta-graph AttentionWeb and Big Data10.1007/978-981-97-7238-4_9(129-144)Online publication date: 31-Aug-2024
https://dl.acm.org/doi/10.1007/978-981-97-7238-4_9

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents