research-article

Partial Tubal Nuclear Norm Regularized Multi-view Learning

Authors:

Yicong ZhouAuthors Info & Claims

MM '21: Proceedings of the 29th ACM International Conference on Multimedia

Pages 1341 - 1349

https://doi.org/10.1145/3474085.3475699

Published: 17 October 2021 Publication History

Abstract

Multi-view clustering and multi-view dimension reduction explore ubiquitous and complementary information between multiple features to enhance the clustering, recognition performance. However, multi-view clustering and multi-view dimension reduction are treated independently, ignoring the underlying correlations between them. In addition, previous methods mainly focus on using the tensor nuclear norm for low-rank representation to explore the high correlation of multi-view features, which often causes the estimation bias of the tensor rank. To overcome these limitations, we propose the partial tubal nuclear norm regularized multi-view learning (PTN2ML) method, in which the partial tubal nuclear norm as a non-convex surrogate of the tensor tubal multi-rank, only minimizes the partial sum of the smaller tubal singular values to preserve the low-rank property of the self-representation tensor. PTN2ML pursues the latent representation from the projection space rather than from the input space to reveal the structural consensus and suppress the disturbance of noisy data. The proposed method can be efficiently optimized by the alternating direction method of multipliers. Extensive experiments, including multi-view clustering and multi-view dimension reduction substantiate the superiority of the proposed methods beyond state-of-the-arts.

References

[1]

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

[2]

Ying Chen, Chun-Guang Li, and Chong You. 2020 a. Stochastic sparse subspace clustering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 4155--4164.

[3]

Yongyong Chen, Shuqin Wang, Chong Peng, Zhongyun Hua, and Yicong Zhou. 2021 a. Generalized Nonconvex Low-rank Tensor Approximation for Mutli-view Subspace Clustering. IEEE Transactions on Image Processing, Vol. 30 (2021), 4022--4035.

[4]

Yongyong Chen, Shuqin Wang, Fangying Zheng, and Yigang Cen. 2020 b. Graph-regularized least squares regression for multi-view subspace clustering. Knowledge-Based Systems, Vol. 194 (2020), 105482.

[5]

Yongyong Chen, Xiaolin Xiao, Zhongyun Hua, and Yicong Zhou. 2021 b. Adaptive Transition Probability Matrix Learning for Multiview Spectral Clustering. IEEE Transactions on Neural Networks and Learning Systems (2021).

[6]

Yongyong Chen, Xiaolin Xiao, Chong Peng, Guangming Lu, and Yicong Zhou. 2021 c. Low-Rank Tensor Graph Learning for Multi-view Subspace Clustering. IEEE Transactions on Circuits and Systems for Video Technology (2021).

[7]

Zhengming Ding, Ming Shao, and Yun Fu. 2018. Robust Multi-view Representation: A Unified Perspective from Multi-view Learning to Domain Adaption. In Twenty-Seventh International Joint Conference on Artificial Intelligence. 5434--5440.

Digital Library

[8]

Nazli Ikizler, R Gokberk Cinbis, Selen Pehlivan, and Pinar Duygulu. 2008. Recognizing actions from still images. In 19th International Conference on Pattern Recognition. IEEE, 1--4.

[9]

Yangbangyan Jiang, Zhiyong Yang, Qianqian Xu, Xiaochun Cao, and Qingming Huang. 2018. When to learn what: Deep cognitive subspace clustering. In Proceedings of the 26th ACM International Conference on Multimedia. 718--726.

Digital Library

[10]

Misha E Kilmer and Carla D Martin. 2011. Factorization strategies for third-order tensors. Linear Algebra Appl., Vol. 435, 3 (2011), 641--658.

[11]

Fei-Fei Li and Perona Pietro. 2005. A bayesian hierarchical model for learning natural scene categories. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2. IEEE, 524--531.

Digital Library

[12]

Ruihuang Li, Changqing Zhang, Qinghua Hu, Pengfei Zhu, and Zheng Wang. 2019. Flexible Multi-View Representation Learning for Subspace Clustering. In Twenty-Eighth International Joint Conference on Artificial Intelligence. 2916--2922.

Digital Library

[13]

Guangcan Liu, Zhouchen Lin, Shuicheng Yan, Ju Sun, Yong Yu, and Yi Ma. 2013. Robust recovery of subspace structures by low-rank representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, 1 (2013), 171--184.

Digital Library

[14]

Khanh Luong and Richi Nayak. 2020. A Novel Approach to Learning Consensus and Complementary Information for Multi-View Data Clustering. In IEEE 36th International Conference on Data Engineering. 865--876.

[15]

Brbić M and Kopriva I. 2018. Multi-view low-rank sparse subspace clustering. Pattern Recognition, Vol. 73 (2018), 247--258.

[16]

Zhengrui Ma, Zhao Kang, Guangchun Luo, Ling Tian, and Wenyu Chen. 2020. Towards Clustering-friendly Representations: Subspace Clustering via Graph Filtering. In Proceedings of the 28th ACM International Conference on Multimedia. 3081--3089.

Digital Library

[17]

Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research, Vol. 9, Nov (2008), 2579--2605.

[18]

Andrew Y Ng, Michael I Jordan, and Yair Weiss. 2002 a. On spectral clustering: Analysis and an algorithm. In Advances in Neural Information Processing Systems. 849--856.

Digital Library

[19]

Andrew Y Ng, Michael I Jordan, and Yair Weiss. 2002 b. On spectral clustering: Analysis and an algorithm. In Advances in Neural Information Processing Systems. 849--856.

Digital Library

[20]

Feiping Nie, Guohao Cai, Jing Li, and Xuelong Li. 2018. Auto-weighted multi-view learning for image clustering and semi-supervised classification. IEEE Transactions on Image Processing, Vol. 27, 3 (2018), 1501--1511.

[21]

Feiping Nie, Heng Huang, Xiao Cai, and Chris H Ding. 2010. Efficient and robust feature selection via joint l2,1norms minimization. In Advances in Neural Information Processing Systems. 1813--1821.

Digital Library

[22]

Tae-Hyun Oh, Yu-Wing Tai, Jean-Charles Bazin, Hyeongwoo Kim, and In So Kweon. 2015. Partial sum minimization of singular values in robust PCA: Algorithm and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 38, 4 (2015), 744--758.

Digital Library

[23]

Ariadna Quattoni and Antonio Torralba. 2009. Recognizing indoor scenes. In Proceed-ings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 413--420.

[24]

Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. In Proceedings of International Conference on Learning Representations.

[25]

Hao Wang, Yan Yang, and Bing Liu. 2020. protect GMC: Graph-based Multi-view Clustering. IEEE Transactions on Knowledge and Data Engineering, Vol. 32, 6 (2020), 1116--1129.

[26]

Lichen Wang, Zhengming Ding, Zhiqiang Tao, Yunyu Liu, and Yun Fu. 2019. Generative multi-view human action recognition. In Proceedings of the IEEE International Conference on Computer Vision. 6212--6221.

[27]

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 Conference on Computer Vision and Pattern Recognition. 923--931.

[28]

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

[29]

Wai Keung Wong, Zhihui Lai, Jiajun Wen, Xiaozhao Fang, and Yuwu Lu. 2017. Low-rank embedding for robust image feature extraction. IEEE Transactions Image Processing, Vol. 26, 6 (2017), 2905--2917.

Digital Library

[30]

J Wu, Z Lin, and H Zha. 2019. Essential Tensor Learning for Multi-View Spectral Clustering. IEEE Transactions on Image Processing, Vol. 28, 12 (2019), 5910--5922.

[31]

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.

Digital Library

[32]

Deyan Xie, Xiangdong Zhang, Quanxue Gao, Jiale Han, Song Xiao, and Xinbo Gao. 2019. Multiview Clustering by Joint Latent Representation and Similarity Learning. IEEE Transactions on Cybernetics, Vol. 50, 11 (2019), 4848--4854.

[33]

Luofeng Xie, Ming Yin, Xiangyun Yin, Yun Liu, and Guofu Yin. 2018b. Low-rank sparse preserving projections for dimensionality reduction. IEEE Transactions Image Processing, Vol. 27, 11 (2018), 5261--5274.

[34]

Yuan Xie, Dacheng Tao, Wensheng Zhang, Yan Liu, Lei Zhang, and Yanyun Qu. 2018a. On unifying multi-view self-representations for clustering by tensor multi-rank minimization. International Journal of Computer Vision, Vol. 126, 11 (2018), 1157--1179.

Digital Library

[35]

Wanqi Yang, Yinghuan Shi, Yang Gao, Lei Wang, and Ming Yang. 2018. Incomplete-data oriented multiview dimension reduction via sparse low-rank representation. IEEE Transactions on Neural Networks and Learning Systems, Vol. 29, 12 (2018), 6276--6291.

[36]

Bin Zhang, Qianyao Qiang, Fei Wang, and Feiping Nie. 2021. Flexible Multi-view Unsupervised Graph Embedding. IEEE Transactions on Image Processing, Vol. 30 (2021), 4143--4156.

[37]

Changqing Zhang, Huazhu Fu, Qinghua Hu, Xiaochun Cao, Yuan Xie, Dacheng Tao, and Dong Xu. 2020 a. Generalized latent multi-view subspace clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 42, 1 (2020), 86--99.

Digital Library

[38]

Changqing Zhang, Huazhu Fu, Qinghua Hu, Pengfei Zhu, and Xiaochun Cao. 2017a. Flexible multi-view dimensionality co-reduction. IEEE Transactions Image Processing, Vol. 26, 2 (2017), 648--659.

Digital Library

[39]

Changqing Zhang, Huazhu Fu, Si Liu, Guangcan Liu, and Xiaochun Cao. 2015. Low-rank tensor constrained multiview subspace clustering. In Proceedings of the IEEE International Conference on Computer Vision. 1582--1590.

Digital Library

[40]

Pei Zhang, Xinwang Liu, Jian Xiong, Sihang Zhou, Wentao Zhao, En Zhu, and Zhiping Cai. 2020 b. Consensus One-step Multi-view Subspace Clustering. IEEE Transactions on Knowledge and Data Engineering (2020).

[41]

Yupei Zhang, Ming Xiang, and Bo Yang. 2017b. Low-rank preserving embedding. Pattern Recognition, Vol. 70 (2017), 112--125.

[42]

YiFan Zhang, Changsheng Xu, Hanqing Lu, and Yeh-Min Huang. 2009. Character identification in feature-length films using global face-name matching. IEEE Transactions on Multimedia, Vol. 11, 7 (2009), 1276--1288.

Digital Library

Cited By

Chen YZhao YWang SChen JZhang Z(2024)Partial Tubal Nuclear Norm-Regularized Multiview Subspace LearningIEEE Transactions on Cybernetics10.1109/TCYB.2023.326317554:6(3777-3790)Online publication date: Jun-2024
https://doi.org/10.1109/TCYB.2023.3263175
Zhang CXue HNie KWu XLou ZYang SZhou QHu S(2024)Nice to meet images with Big Clusters and Features: A cluster-weighted multi-modal co-clustering methodInformation Processing & Management10.1016/j.ipm.2024.10373561:5(103735)Online publication date: Sep-2024
https://doi.org/10.1016/j.ipm.2024.103735
Chen ZFu LXiao SWang SPlant CGuo W(2023)Multi-View Graph Convolutional Networks with Differentiable Node SelectionACM Transactions on Knowledge Discovery from Data10.1145/360895418:1(1-21)Online publication date: 10-Aug-2023
https://dl.acm.org/doi/10.1145/3608954
Show More Cited By

Index Terms

Partial Tubal Nuclear Norm Regularized Multi-view Learning
1. Computing methodologies

Recommendations

Multi-dimensional imaging data recovery via minimizing the partial sum of tubal nuclear norm
Abstract
In this paper, we investigate tensor recovery problems within the tensor singular value decomposition (t-SVD) framework. We propose the partial sum of the tubal nuclear norm (PSTNN) of a tensor. The PSTNN is a surrogate of the tensor ...
Highlights
- We propose the partial sum of the tubal nuclear norm (PSTNN) for tensor recovery.
Unifying tensor factorization and tensor nuclear norm approaches for low-rank tensor completion
Abstract
Low-rank tensor completion (LRTC) has gained significant attention due to its powerful capability of recovering missing entries. However, it has to repeatedly calculate the time-consuming singular value decomposition (SVD). To address ...
Latent multi-view self-representations for clustering via the tensor nuclear norm
Abstract
How to design effective multi-view subspace clustering (MVSC) algorithms has recently become a research hotspot. In this paper, we propose a new MVSC algorithm, termed latent multi-view self-representation for clustering via the tensor nuclear ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

MM '21: Proceedings of the 29th ACM International Conference on Multimedia

October 2021

5796 pages

ISBN:9781450386517

DOI:10.1145/3474085

General Chairs:
Heng Tao Shen
University of Electronic Science&Technology of China, China
,
Yueting Zhuang
Zhejiang University, China
,
John R. Smith
IBM, USA
,
Program Chairs:
Yang Yang
University of Electronic Science and Technology of China, China
,
Pablo Cesar
CWI&TU Delft, The Netherlands
,
Florian Metze
FACEBOOK, Inc., USA
,
Balakrishnan Prabhakaran
University of Texas at Dallas, USA

Copyright © 2021 ACM.

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 ACM 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]

Sponsors

SIGMM: ACM Special Interest Group on Multimedia

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 October 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

the Shenzhen College Stability Support Plan
the Guangdong Basic and Applied Basic Research Foundation
University of Macau
the Shenzhen Key Technical Project

Conference

MM '21

Sponsor:

SIGMM

MM '21: ACM Multimedia Conference

October 20 - 24, 2021

Virtual Event, China

Acceptance Rates

Overall Acceptance Rate 554 of 2,551 submissions, 22%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
250
Total Downloads

Downloads (Last 12 months)29
Downloads (Last 6 weeks)7

Reflects downloads up to 24 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Chen YZhao YWang SChen JZhang Z(2024)Partial Tubal Nuclear Norm-Regularized Multiview Subspace LearningIEEE Transactions on Cybernetics10.1109/TCYB.2023.326317554:6(3777-3790)Online publication date: Jun-2024
https://doi.org/10.1109/TCYB.2023.3263175
Zhang CXue HNie KWu XLou ZYang SZhou QHu S(2024)Nice to meet images with Big Clusters and Features: A cluster-weighted multi-modal co-clustering methodInformation Processing & Management10.1016/j.ipm.2024.10373561:5(103735)Online publication date: Sep-2024
https://doi.org/10.1016/j.ipm.2024.103735
Chen ZFu LXiao SWang SPlant CGuo W(2023)Multi-View Graph Convolutional Networks with Differentiable Node SelectionACM Transactions on Knowledge Discovery from Data10.1145/360895418:1(1-21)Online publication date: 10-Aug-2023
https://dl.acm.org/doi/10.1145/3608954
Wang SChen YLin ZCen YCao Q(2023)Robustness Meets Low-Rankness: Unified Entropy and Tensor Learning for Multi-View Subspace ClusteringIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2023.326680133:11(6302-6316)Online publication date: 13-Apr-2023
https://dl.acm.org/doi/10.1109/TCSVT.2023.3266801
Zhang XZhang YLiu ZWang H(2023)Three-dimensional seismic data reconstruction via partial sum of tensor nuclear norm minimizationJournal of Geophysics and Engineering10.1093/jge/gxad01220:2(376-386)Online publication date: 1-Mar-2023
https://doi.org/10.1093/jge/gxad012

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 Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten