Skip to main content
SpringerLink
Log in

Robust graph-based multi-view clustering in latent embedding space

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

Multi-view graph-based clustering (MGC) aims to cluster multi-view data via a graph learning scheme, and has aroused widespread research interests in behavior detection, face recognition, and information retrieval in recent years. However, most of the existing MGC methods usually learn the affinity graph in the original space, such that they are inevitably hindered by the curse of dimensionality and corrupted features. Moreover, they usually learn the affinity between paired-samples by using Euclidean-distance metric; nevertheless, such a metric is sensitive to noise and outliers. In this paper, we propose a novel MGC method, namely latent embedding space learning (LESL), which aims to learn a latent embedding space and a robust affinity graph simultaneously. Specifically, a latent embedding representation is firstly learned, which can reduce the corruption and redundancy of the original views, and can effectively utilize the complementary information of multiple views. Afterwards, a robust estimator is used to automatically cut the connections among inter-cluster in the affinity graph. Finally, alternating direction minimization on the augmented Lagrangian multiplier (ALM-ADM) is adopted to optimize the unified objective function. Experimental results show that LESL outperforms state-of-the-art methods obviously.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. http://archive.ics.uci.edu/ml/datasets/Multiple+Features

  2. http://www.cl.cam.ac.uk/research/dtg/

References

  1. Mekthanavanh V, Li T, Meng H, Yang Y, Jie H (2019) Social web video clustering based on multi-view clustering via nonnegative matrix factorization. Int J Mach Learn Cybern 10(10):2779–2790

    Article  Google Scholar 

  2. Zhao J, Xie X, Xin X, Sun S (2017) Multi-view learning overview: recent progress and new challenges. Inf Fusion 38:43–54

    Article  Google Scholar 

  3. Brbić M, Kopriva I (2018) Multi-view low-rank sparse subspace clustering. Pattern Recogn 73:247–258

    Article  Google Scholar 

  4. Qaddoura R, Faris H, Aljarah I (2020) An efficient clustering algorithm based on the k-nearest neighbors with an indexing ratio. Int J Mach Learn Cybern 11(3):675–714

    Article  Google Scholar 

  5. Kumar A, Daumé H (2011) A co-training approach for multi-view spectral clustering. In: Proceedings of the 28th international conference on machine learning (ICML-11), pp 393–400

  6. Wang W, Zhou Z-H (2007) Analyzing co-training style algorithms. In: European conference on machine learning, Springer, pp 454–465

  7. Zhang Z, Zhou J (2012) Multi-task clustering via domain adaptation. Pattern Recogn 45(1):465–473

    Article  Google Scholar 

  8. Ren Z, Sun Q (2020) Simultaneous global and local graph structure preserving for multiple kernel clustering. IEEE Trans Neural Netw Learn Syst 32(5):1839–1851

    Article  MathSciNet  Google Scholar 

  9. Ren Z, Yang SX, Sun Q, Wang T (2020) Consensus affinity graph learning for multiple kernel clustering. IEEE Trans Cybern 51(6):3273–3284

    Article  Google Scholar 

  10. Ren Z, Mukherjee M, Bennis M, Lloret J (2020) Multi-kernel clustering via non-negative matrix factorization tailored graph tensor over distributed networks. IEEE J Sel Areas Commun 39(7):1946–1956

    Article  Google Scholar 

  11. Ren Z, Mukherjee M, Lloret J, Venu P (2020) Multiple kernel driven clustering with locally consistent and selfish graph in industrial Iot. IEEE Trans Ind Inform 17(4):2956–2963

    Article  Google Scholar 

  12. Ren Z, Sun Q, Wei D (2021) Multiple kernel clustering with kernel k-means coupled graph tensor learning. In: Proceedings of the AAAI Conference on Artificial Intelligence 35(11):9411–9418

  13. Ma G, He L, Lu C-T, Shao W, Yu, PS Leow AD, Ragin AB (2017) Multi-view clustering with graph embedding for connectome analysis. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp 127–136

  14. Zhang X, Gao H, Li G, Zhao J, Huo J (2018) Multi-view clustering based on graph-regularized nonnegative matrix factorization for object recognition. Inf Sci 432:463–478

    Article  MathSciNet  Google Scholar 

  15. Kang Z, Wen L, Chen W, Zenglin X (2019) Low-rank kernel learning for graph-based clustering. Knowl Based Syst 163(1):510–517

    Article  Google Scholar 

  16. Zhang C, Hu Q, Fu H, Zhu P, Cao X (2017) Latent multi-view subspace clustering. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4279–4287

  17. Zhang C, Huazhu F, Qinghua H, Cao X, Xie Y, Tao D, Dong X (2018) Generalized latent multi-view subspace clustering. IEEE Trans Pattern Anal Mach Intell 42(1):86–99

    Article  Google Scholar 

  18. Zhang C, Cui Y, Han Z, Joey TZ, Huazhu F, Qinghua H (2020) Deep partial multi-view learning. IEEE Trans Pattern Anal Mach Intell 1:1–1. https://doi.org/10.1109/TPAMI.2020.3037734

    Google Scholar 

  19. Zhang C, Fu H, Zhou JT, Hu Q et al (2019) Cpm-nets: cross partial multi-view networks. Advances in neural information processing systems. In: Proceedings of the 33rd International Conference on Neural Information Processing Systems,  pp 559–569

  20. Nie F, Wang X, Huang H (2014) Clustering and projected clustering with adaptive neighbors. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 977–986

  21. Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905

    Article  Google Scholar 

  22. Saha M (2013) A graph based approach to multiview clustering. In: International conference on pattern recognition and machine intelligence, Springer, pp 128–133

  23. Nie F, Li J, Li X (2017) Self-weighted multiview clustering with multiple graphs. In: Proceedings of the 26th international joint conference on artificial intelligence, pp 2564–2570

  24. Shu Z, Xiaojun W, Honghui FP, Huang DW, Cong H, Ye F (2017) Parameter-less auto-weighted multiple graph regularized nonnegative matrix factorization for data representation. Knowl Based Syst 131:105–112

    Article  Google Scholar 

  25. Wang H, Yang Y, Liu B (2019) Gmc: Graph-based multi-view clustering. IEEE Trans Knowl Data Eng 32(6):1116–1129

    Article  Google Scholar 

  26. Zhan K, Zhang C, Guan J, Wang J (2017) Graph learning for multiview clustering. IEEE Trans Cybern 48(10):2887–2895

    Article  Google Scholar 

  27. Zhuge W, Nie F, Hou C, Yi D (2017) Unsupervised single and multiple views feature extraction with structured graph. IEEE Trans Knowl Data Eng 29(10):2347–2359

    Article  Google Scholar 

  28. Wang H, Yang Y, Liu B, Fujita H (2019) A study of graph-based system for multi-view clustering. Knowl Based Syst 163:1009–1019

    Article  Google Scholar 

  29. Nie F, Li J, Li X (2013) Parameter-free auto-weighted multiple graph learning: a framework for multiview clustering and semi-supervised classification. In: Proceedings of the twenty-fifth international joint conference on artificial intelligence, pp 1881–1887

  30. Nie F, Cai G, Li X (2017) Multi-view clustering and semi-supervised classification with adaptive neighbours. In: Proceedings of the AAAI conference on artificial intelligence, vol 31

  31. Chen M-S, Huang L, Wang C-D, Huang D (2020) Multi-view clustering in latent embedding space. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 3513–3520

  32. Chen M-S, Huang L, Wang C-D, Huang D, Lai J-H (2021) Relaxed multi-view clustering in latent embedding space. Inf Fusion 68:8–21

    Article  Google Scholar 

  33. Sohil AS, Vladlen K (2017) Robust continuous clustering. Proc Natl Acad Sci 114(37):9814–9819

    Article  Google Scholar 

  34. Ma J, Wang R, Ji W, Zhao J, Zong M, Gilman A (2018) Robust multi-view continuous subspace clustering. Pattern Recogn Lett

  35. Guo W (2021) Sparse dual graph-regularized deep nonnegative matrix factorization for image clustering. IEEE Access 9:39926–39938

    Article  Google Scholar 

  36. Huang S, Kang Z, Zenglin X (2020) Auto-weighted multi-view clustering via deep matrix decomposition. Pattern Recogn 97:107015

    Article  Google Scholar 

  37. Chang S, Jie H, Li T, Wang H, Peng B (2021) Multi-view clustering via deep concept factorization. Knowl Based Syst 217:106807

    Article  Google Scholar 

  38. Wei S, Wang J, Guoxian Y, Domeniconi C, Zhang X (2020) Multi-view multiple clusterings using deep matrix factorization. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 6348–6355

  39. Nie F, Wang X, Jordan M, Huang H (2016) The constrained laplacian rank algorithm for graph-based clustering. In: Proceedings of the AAAI conference on artificial intelligence, vol 30, pp 1969–1976

  40. Fan K (1949) On a theorem of weyl concerning eigenvalues of linear transformations i. Proc Natl Acad Sci USA 35(11):652

    Article  MathSciNet  Google Scholar 

  41. Wachspress EL (1988) Iterative solution of the lyapunov matrix equation. Appl Math Lett 1(1):87–90

    Article  MathSciNet  Google Scholar 

  42. Ng A, Jordan M, Weiss Y (2001) On spectral clustering: analysis and an algorithm. Adv Neural Inf Process Syst 14:849–856

    Google Scholar 

  43. Xia R, Pan Y, Du L, Yin J (2014) Robust multi-view spectral clustering via low-rank and sparse decomposition. In: International conference on artificial intelligence, pp 2149–2155

  44. Lampert CH, Nickisch H, Harmeling S (2014) Attribute-based classification for zero-shot visual object categorization. IEEE Trans Pattern Anal Mach Intell 36(3):453–465

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by the Sichuan Science and Technology Program (Grant no. 2021YJ0083), the State Key Lab. Foundation for Novel Software Technology of Nanjing University (Grant no. KFKT2021B23), the Key Lab of Film and TV Media Technology of Zhejiang Province (Grant no. 2020E10015), the Natural Science Foundation Project of CQ CSTC (Grant no. cstc2020jcyj-msxmX0473), the Scientific Research Fund of Sichuan Provincial Education Department (Grant no. 17ZB0441), the National Natural Science Foundation of China (Grant no. 61601382), and the Zhejiang Provincial Natural Science Foundation of China (Grant no. LGF21F020003).

Author information

Authors and Affiliations

  1. School of Information Engineering, Southwest University of Science and Technology, Mianyang, 621010, China

    Yanying Mei, Bin Wu, Yanhua Shao & Tao Yang

  2. School of National Defence Science and Technology, Southwest University of Science and Technology, Mianyang, 621010, China

    Zhenwen Ren

  3. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210008, China

    Zhenwen Ren

Authors
  1. Yanying Mei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhenwen Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bin Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yanhua Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tao Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Zhenwen Ren or Bin Wu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mei, Y., Ren, Z., Wu, B. et al. Robust graph-based multi-view clustering in latent embedding space. Int. J. Mach. Learn. & Cyber. 13, 497–508 (2022). https://doi.org/10.1007/s13042-021-01421-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-021-01421-6

Keywords

Search

Navigation