research-article

Incomplete multi-view clustering with incomplete graph-regularized orthogonal non-negative matrix factorization

Authors:

Wei HanAuthors Info & Claims

Applied Intelligence, Volume 52, Issue 13

Pages 14607 - 14623

https://doi.org/10.1007/s10489-022-03551-y

Published: 01 October 2022 Publication History

Abstract

Incomplete multi-view clustering (IMC) has achieved widespread attention due to its advantage in fusing the multi-view information when the view samples are unobserved partly. Recently, it is shown that the clustering performance in the subspace can be improved by preserving the clustering structure of each view, but the problem of the inconsistent clustering structure caused by the incomplete graphs are seldom considered, restricting the clustering performance. Motivated by the clustering interpretation of the orthogonal non-negative matrix factorization, it is employed to unify the clustering structure of the data, and a new model called Incomplete Graph-regularized Orthogonal Non-negative Matrix Factorization (IGONMF) is proposed in this paper. In IGONMF, the reproduced representation is developed, based on which, a set of incomplete graphs are utilized to fully take advantage of the geometric structure of the data. And the orthogonality is further employed to alleviate the problem of the inconsistent clustering structure. Also, we design an effective iterative updating algorithm to solve the proposed model, along with its analysis on the convergence and the computational cost. Finally, experimental results on several real-world datasets indicate that our method is superior to the related state-of-the-art methods.

References

[1]

Khan A and Maji PApproximate graph laplacians for multimodal data clusteringIEEE Trans Pattern Anal Mach Intell2021433798-813https://doi.org/10.1109/TPAMI.2019.2945574

[2]

Sun D, Toh K-C, and Yuan Y Convex clustering: Model, theoretical guarantee and efficient algorithm J Mach Learn Res 2021 22 9 1-32 http://jmlr.org/papers/v22/18-694.html

[3]

Li X, Zhang R, Wang Q, and Zhang HAutoencoder constrained clustering with adaptive neighborsIEEE Trans Neural Netw Learn Syst2021321443-449https://doi.org/10.1109/TNNLS.2020.2978389

[4]

Wang Z, Li Z, Wang R, Nie F, and Li XLarge graph clustering with simultaneous spectral embedding and discretizationIEEE Trans Pattern Anal Mach Intell202143124426-4440https://doi.org/10.1109/TPAMI.2020.3002587

[5]

Yang Z, Li Q, Liu W, and Lv JShared multi-view data representation for multi-domain event detectionIEEE Trans Pattern Anal Mach Intell20204251243-1256https://doi.org/10.1109/TPAMI.2019.2893953

[6]

Chao G, Sun S, and Bi JA survey on multiview clusteringIEEE Trans Artif Intell202122146-168https://doi.org/10.1109/TAI.2021.3065894

[7]

Zhang X, Yang Y, Li T, Zhang Y, Wang H, and Fujita HCMC: A consensus multi-view clustering model for predicting alzheimer’s disease progressionComput Methods Programs Biomed2021199105895https://doi.org/10.1016/j.cmpb.2020.105895

[8]

Wang Q, Chen M, Nie F, and Li XDetecting coherent groups in crowd scenes by multiview clusteringIEEE Trans Pattern Anal Mach Intell202042146-58https://doi.org/10.1109/TPAMI.2018.2875002

[9]

Peng B, Lei J, Fu H, Shao L, and Huang QA recursive constrained framework for unsupervised video action clusteringIEEE Trans Ind Inf2020161555-565https://doi.org/10.1109/TII.2019.2937514

[10]

Zhang Z, Zhai Z, and Li LUniform projection for multi-view learningIEEE Trans Pattern Anal Mach Intell20173981675-1689https://doi.org/10.1109/TPAMI.2016.2601608

[11]

Ma F, Meng D, Dong X, and Yang Y Self-paced multi-view co-training J Mach Learn Res 2020 21 57 1-38 http://jmlr.org/papers/v21/18-794.html

[12]

Khan A, Maji P (2021) Multi-manifold optimization for multi-view subspace clustering. IEEE Transactions on Neural Networks and Learning Systems.

[13]

Nie F, Cai G, Li J, and Li XAuto-weighted multi-view learning for image clustering and semi-supervised classificationIEEE Trans Image Process20182731501-1511https://doi.org/10.1109/TIP.2017.2754939

[14]

Wang H, Yang Y, and Liu BGMC: graph-based multi-view clusteringIEEE Trans Knowl Data Eng20203261116-1129https://doi.org/10.1109/TKDE.2019.2903810

[15]

Li X, Zhang H, Wang R, and Nie FMultiview clustering: A scalable and parameter-free bipartite graph fusion methodIEEE Trans Pattern Anal Mach Intell2022441330-344https://doi.org/10.1109/TPAMI.2020.3011148

[16]

Zhan K, Nie F, Wang J, and Yang YMultiview consensus graph clusteringIEEE Trans Image Process20192831261-1270https://doi.org/10.1109/TIP.2018.2877335

[17]

Wang H, Yang Y, Liu B, and Fujita HA study of graph-based system for multi-view clusteringKnowl Based Syst20191631009-1019https://doi.org/10.1016/j.knosys.2018.10.022

[18]

Li Z, Zhao H, Guo Y, Yang Z, Xie S (2021) Accelerated log-regularized convolutional transform learning and its convergence guarantee. IEEE Transactions on Cybernetics.

[19]

Zhang B, Qiang Q, Wang F, and Nie FFast multi-view semi-supervised learning with learned graphIEEE Trans Knowl Data Eng2022341286-299https://doi.org/10.1109/TKDE.2020.2978844

[20]

Yi Z, Yang Y, Li T, and Fujita HA multitask multiview clustering algorithm in heterogeneous situations based on LLE and LEKnowl Based Syst2019163776-786https://doi.org/10.1016/j.knosys.2018.10.001

[21]

Yang Z, Zhang Y, Xiang Y, Yan W, and Xie SNon-negative matrix factorization with dual constraints for image clusteringIEEE Trans Syst Man Cybern: Syst20205072524-2533https://doi.org/10.1109/TSMC.2018.2820084

[22]

Wang J, Tian F, Yu H, Liu CH, Zhan K, and Wang XDiverse non-negative matrix factorization for multiview data representationIEEE Trans Cybern20184892620-2632https://doi.org/10.1109/TCYB.2017.2747400

[23]

Liang N, Yang Z, Li Z, Sun W, and Xie SMulti-view clustering by non-negative matrix factorization with co-orthogonal constraintsKnowl-Based Syst2020194105582https://doi.org/10.1016/j.knosys.2020.105582

[24]

Wang X, Zhang T, and Gao XMultiview clustering based on non-negative matrix factorization and pairwise measurementsIEEE Trans Cybern20194993333-3346https://doi.org/10.1109/TCYB.2018.2842052

[25]

Yang Z, Liang N, Yan W, Li Z, and Xie SUniform distribution non-negative matrix factorization for multiview clusteringIEEE Trans Cybern20215163249-3262https://doi.org/10.1109/TCYB.2020.2984552

[26]

Zhao W, Xu C, Guan Z, and Liu YMultiview concept learning via deep matrix factorizationIEEE Trans Neural Netw Learn Syst2021322814-825https://doi.org/10.1109/TNNLS.2020.2979532

[27]

Wang D, Han S, Wang Q, He L, Tian Y, Gao X (2021) Pseudo-label guided collective matrix factorization for multiview clustering. IEEE Transactions on Cybernetics.

[28]

Xu C, Tao D, and Xu CMulti-view learning with incomplete viewsIEEE Trans Image Process201524125812-5825https://doi.org/10.1109/TIP.2015.2490539

[29]

Zhang C, Adeli E, Wu Z, Li G, Lin W, and Shen DInfant brain development prediction with latent partial multi-view representation learningIEEE Trans Med Imaging2019384909-918https://doi.org/10.1109/TMI.2018.2874964

[30]

Chao G, Sun J, Lu J, Wang A-L, Langleben DD, Li C-R, and Bi JMulti-view cluster analysis with incomplete data to understand treatment effectsInf Sci2019494278-293https://doi.org/10.1016/j.ins.2019.04.039

[31]

Trivedi A, Rai P, Daumé III H, DuVall S L (2010) Multiview clustering with incomplete views. In: In Advances in neural information processing systems

[32]

Li S-Y, Jiang Y, Zhou Z-H (2014) Partial multi-view clustering. In: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, Québec City, Québec, pp 1968– 1974

[33]

Shao W, He L, Yu P S (2015) Multiple incomplete views clustering via weighted nonnegative matrix factorization with L_2,1 regularization. In: Machine Learning and Knowledge Discovery in Databases - European Conference, Porto., pp 318–334

[34]

Hu M, Chen S (2018) Doubly aligned incomplete multi-view clustering. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, Stockholm, Sweden., pp 2262–2268

[35]

Liang N, Yang Z, Li Z, Xie S, and Su C-YSemi-supervised multi-view clustering with graph-regularized partially shared non-negative matrix factorizationKnowl-Based Syst2020190105185https://doi.org/10.1016/j.knosys.2019.105185

[36]

Wen J, Zhang Z, Xu Y, Zhang B, Fei L, Liu H (2019) Unified embedding alignment with missing views inferring for incomplete multi-view clustering. In: The Thirty-Third AAAI Conference on Artificial Intelligence, Honolulu, Hawaii, USA., pp 5393–5400

[37]

Wen J, Yan K, Zhang Z, Xu Y, Wang J, Fei L, and Zhang BAdaptive graph completion based incomplete multi-view clusteringIEEE Trans Multim2021232493-2504https://doi.org/10.1109/TMM.2020.3013408

[38]

Yang L, Shen C, Hu Q, Jing L, and Li YAdaptive sample-level graph combination for partial multiview clusteringIEEE Trans Image Process2020292780-2794https://doi.org/10.1109/TIP.2019.2952696

[39]

Rai N, Negi S, Chaudhury S, Deshmukh O (2016) Partial multi-view clustering using graph regularized NMF. In: 23rd International Conference on Pattern Recognition, Cancún, Mexico., pp 2192–2197

[40]

Wen J, Zhang Z, Zhang Z, Fei L, and Wang MGeneralized incomplete multiview clustering with flexible locality structure diffusionIEEE Trans Cybern2021511101-114https://doi.org/10.1109/TCYB.2020.2987164

[41]

Lee DD and Seung HS Learning the parts of objects by non-negative matrix factorization Nature 1999 401 6755 788-791

[42]

Ding C, Li T, Peng W, Park H (2006) Orthogonal nonnegative matrix t-factorizations for clustering. In: Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining., Philadelphia, pp 126–135

[43]

Li B, Zhou G, and Cichocki ATwo efficient algorithms for approximately orthogonal nonnegative matrix factorizationIEEE Signal Process Lett2015227843-846https://doi.org/10.1109/LSP.2014.2371895

[44]

Li Z, Wu X, and Peng HNonnegative matrix factorization on orthogonal subspacePattern Recogn Lett2010319905-911https://doi.org/10.1016/j.patrec.2009.12.023

[45]

Ma L, Li H, Meng F, Wu Q, and Ngan KNLearning efficient binary codes from high-level feature representations for multilabel image retrievalIEEE Trans Multimed201719112545-2560https://doi.org/10.1109/TMM.2017.2703089

[46]

Tao H, Hou C, Yi D, and Zhu JMultiview classification with cohesion and diversityIEEE Trans Cybern20205052124-2137https://doi.org/10.1109/TCYB.2018.2881474

[47]

Liang N, Yang Z, Li Z, Xie S, and Sun WSemi-supervised multi-view learning by using label propagation based non-negative matrix factorizationKnowl-Based Syst2021228107244https://doi.org/10.1016/j.knosys.2021.107244

[48]

Demsar J Statistical comparisons of classifiers over multiple data sets J Mach Learn Res 2006 7 1-30 http://jmlr.org/papers/v7/demsar06a.html

Cited By

Zhou LDu GLü KWang LDu J(2024)A Survey and an Empirical Evaluation of Multi-View Clustering ApproachesACM Computing Surveys10.1145/364510856:7(1-38)Online publication date: 8-Feb-2024
https://dl.acm.org/doi/10.1145/3645108
Ma ZWang JLi HHuang Y(2023)Adaptive graph regularized non-negative matrix factorization with self-weighted learning for data clusteringApplied Intelligence10.1007/s10489-023-04868-y53:23(28054-28073)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s10489-023-04868-y
Jing WLu LLiu Q(2023)Graph regularized discriminative nonnegative tucker decomposition for tensor data representationApplied Intelligence10.1007/s10489-023-04738-753:20(23864-23882)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1007/s10489-023-04738-7
Show More Cited By

Index Terms

Incomplete multi-view clustering with incomplete graph-regularized orthogonal non-negative matrix factorization
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Information systems
  1. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Applied Intelligence

Applied Intelligence Volume 52, Issue 13

Oct 2022

1143 pages

ISSN:0924-669X

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© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 October 2022

Accepted: 22 March 2022

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Cited By

Zhou LDu GLü KWang LDu J(2024)A Survey and an Empirical Evaluation of Multi-View Clustering ApproachesACM Computing Surveys10.1145/364510856:7(1-38)Online publication date: 8-Feb-2024
https://dl.acm.org/doi/10.1145/3645108
Ma ZWang JLi HHuang Y(2023)Adaptive graph regularized non-negative matrix factorization with self-weighted learning for data clusteringApplied Intelligence10.1007/s10489-023-04868-y53:23(28054-28073)Online publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1007/s10489-023-04868-y
Jing WLu LLiu Q(2023)Graph regularized discriminative nonnegative tucker decomposition for tensor data representationApplied Intelligence10.1007/s10489-023-04738-753:20(23864-23882)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1007/s10489-023-04738-7
Zhang JFei LLi YNie FJiang QLiang LYan P(2022)Instance-level Weighted Graph Learning for Incomplete Multi-view ClusteringProceedings of the 2022 11th International Conference on Computing and Pattern Recognition10.1145/3581807.3581832(171-178)Online publication date: 17-Nov-2022
https://dl.acm.org/doi/10.1145/3581807.3581832

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