Skip to main content
SpringerLink
Log in

Incomplete multi-view clustering via attention-based contrast learning

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

Abstract

Multi-view clustering (MVC) is an essential and challenging task in machine learning and data mining. In recent years, this field has attracted a lot of attention and achieved remarkable results. The success of multi-view clustering relies heavily on the consistency and integrity of data views to ensure complete data information. In the process of data collection and transmission, data inevitably be lost, which leads to the occurrence of partial view unalignment (VN) and partial view missing (VM). This situation reduces the available information and increases the difficulty of joint learning of multi-view data. To address the incomplete information problem, in this article, we present a novel incomplete multi-view clustering via attention-based contrast learning framework (MCAC) to address the VN and VM puzzles. Due to the diversity of different views, negative samples are formed by randomly selecting some cross-view samples from positive samples, then computing the correlation between local features and latent features for each view by maximizing mutual information and, fusing each specific low-dimensional representation into a joint representation through an attention fusion layer, in addition, adding noise contrast loss reduces or even eliminates the effect of negative samples. MCAC conducts experiments on seven multi-view datasets and demonstrates the effectiveness compared to eleven state-of-the-art methods on the multi-view clustering task.

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data availability

The data sets that support the findings of this study are available from the author upon request.

References

  1. Lu C, Lin Z, Yan S (2016) Convex sparse spectral clustering: single-view to multi-view. IEEE Trans Image Process 25(6):2833–2843

    Article  MathSciNet  MATH  Google Scholar 

  2. Noori M, Bahri A, Mohammadi K (2019) Attention-guided version of 2D unet for automatic brain tumor segmentation. International Conference on Computer and Knowledge Engineering (ICCKE), pp 269–275

  3. Yang M, Li Y, Hu P, Bai J, Lv J, Peng X (2022) Robust multi-view clustering with incomplete information. IEEE Trans Pattern Anal Mach Intell 1–1. https://ieeexplore.ieee.org/document/9723577

  4. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: NIPS (11):6000–6010

  5. Jie W, Zheng Z, Yong X, Zhang B, Fei L (2019) Unified embedding alignment with missing views inferring for incomplete multi-view clustering. Proc AAAI Conf Artif Intell 33(01):5393–5400. https://doi.org/10.1609/aaai.v33i01.33015393

    Article  Google Scholar 

  6. Eric E, Marie D, Sara J (2014) Multi-view constrained clustering with an incomplete mapping between views. Knowl Inf Syst 38(1):231–257

    Article  Google Scholar 

  7. Wen J, Zhang Z, Fei L, Zhang B, Xu Y, Zhang Z, Li J (2022) A survey on incomplete multiview clustering. IEEE Trans Syst 53(2):1136–1149. https://doi.org/10.1109/TSMC.2022.3192635

  8. Li SY, Jiang Y, Zhou ZH (2014) Partial multi-view clustering. Proc AAAI Conf Artif Intell 28(1):1968–1974. https://doi.org/10.1609/aaai.v28i1.8973

    Article  Google Scholar 

  9. Shao W, He L, Yu PS (2015) Multiple incomplete views clustering via weighted nonnegative matrix factorization with L2,1 regularization. ECML/PKDD. Springer, pp 318–334

  10. Chao G, Wang S, Yang S, Li C, Chu D (2022) Incomplete multi-view clustering with multiple imputation and ensemble clustering. Appl Intell. https://doi.org/10.1007/s10489-021-02978-z

    Article  Google Scholar 

  11. Xie M, Ye Z, Pan G, Liu X (2021) Incomplete multi-view subspace clustering with adaptive instance-sample mapping and deep feature fusion. Appl Intell 51(8):5584–5597

    Article  Google Scholar 

  12. Wen J, Xu Y, Liu H (2020) Incomplete multiview spectral clustering with adaptive graph learning. IEEE Trans Cybern 50(4):1418–1429

    Article  Google Scholar 

  13. Xia D, Yang Y, Yang S, Li T (2023) Incomplete multi-view clustering via Kernelized graph learning. Inf Sci 625:1–19

    Article  Google Scholar 

  14. Xia D, Yang Y, Yang S (2023) Incomplete multi-view clustering via auto-weighted fusion in partition space. Tsinghua Sci Technol 28(3):595–611

    Article  Google Scholar 

  15. Xu X, Chen Z, Yin F (2022) Deep mutual information multi-view representation for visual recognition. Appl Intell 52:14888–14904. https://doi.org/10.1007/s10489-022-03462-y

    Article  Google Scholar 

  16. Du G, Zhou L, Yang Y, Lü K, Wang L (2021) Deep multiple auto-encoder-based multi-view clustering. Data Sci Eng 6(3):323–338

    Article  Google Scholar 

  17. Wang Q, Tao Z, Xia W, Gao Q, Cao X, Jiao L (2022) Adversarial multiview clustering networks with adaptive fusion. IEEE Trans Neural Netw Learn Syst 1–13. https://doi.org/10.1109/TNNLS.2022.3145048

  18. Wen J, Zhang Z, Xu Y, Zhang B, Fei L, Xie GS (2020) CDIMC-net: cognitive deep incomplete multi-view clustering network. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, pp 3230–3236

  19. Hyvarinen A, Morioka H (2016) Unsupervised feature extraction by time-contrastive learning and nonlinear ica. NIPS’16, pp 3772–3780

  20. Zhu C, Miao D (2020) Entropy-based multi-view matrix completion for clustering with side information. Pattern Anal Appl 23(1):359–370

    Article  MathSciNet  Google Scholar 

  21. Zhu J, Xia Y, Wu L, Deng J, Zhou W, Qin T, Liu TY, Li H (2023) Masked contrastive representation learning for reinforcement learning. IEEE Trans Pattern Anal Mach Intell 45(3):3421–3433. https://doi.org/10.1109/TPAMI.2022.3176413

  22. Belghazi MI, Baratin A, Rajeswar S, Ozair S, Bengio Y, Courville A, Hjelm RD (2018) Mine: mutual information neural estimation. https://doi.org/10.48550/arXiv.1801.04062

  23. Pirk S, Khansari M, Bai Y, Lynch C, Sermanet P (2019) Online object representations with contrastive learning. https://doi.org/10.48550/arXiv.1906.04312

  24. Yang H, Li J (2023) Label contrastive learning for image classification. Soft Comput. https://doi.org/10.1007/s00500-022-07808-z

    Article  Google Scholar 

  25. Kraskov A, Stögbauer H, Andrzejak RG, Grassberger P (2005) Hierarchical clustering using mutual information. Europhysics Lett (EPL) 70(2):278–284

    Article  MathSciNet  Google Scholar 

  26. Alush A, Friedman A, Goldberger J (2016) Pairwise clustering based on the mutual-information criterion. Neurocomputing 182:284–293

    Article  Google Scholar 

  27. Zhou T, Liu M, Thung KH, Shen D (2019) Latent representation learning for alzheimer’s disease diagnosis with incomplete multi-modality neuroimaging and genetic data. IEEE Trans Med Imaging 38(10):2411–2422. https://doi.org/10.1109/TMI.2019.2913158

  28. Lin Y, Gou Y, Liu Z, Li B, Lv J, Peng X (2021) COMPLETER: incomplete multi-view clustering via contrastive prediction. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 11169–11178

  29. Kumar A, Rai P, Daume H (2011) Co-regularized multi-view spectral clustering. Neural Information Processing Systems. Curran Associates Inc, pp 1413–1421

  30. Vinokourov A, Shawe-Taylor J, Cristianini N (2003) Inferring a semantic representation of text via cross-language correlation analysis. NIPS, pp 1497–1504

  31. Andrew G, Arora R, Bilmes J, Livescu K (2013) Deep canonical correlation analysis. Int Conf Int Conf Mach Learn 28(3):1247–1255

  32. Bach FR, Jordan MI (2002) Kernel independent component analysis. J Mach Learn Res 3(1):1–48

    MathSciNet  MATH  Google Scholar 

  33. Wang W, Arora R, Livescu K, Bilmes J (2016) On deep multi-view representation learning: objectives and optimization. In ICML, pp 1083–1092

  34. Zhang Z, Liu L, Shen F, Shen H, Shao L (2019) Binary multi-view clustering. IEEE Trans Pattern Anal Mach Intell 41(7):1774–1782

    Article  Google Scholar 

  35. Zhang C, Liu Y, Fu H (2019) Ae2-nets: autoencoder in autoencoder networks. In CVPR, pp 2577–2585

  36. Liu X, Li M, Tang C, Xia J, Xiong J, Liu L, Kloft M, Zhu E (2021) Efficient and effective regularized incomplete multi-view clustering. IEEE Trans Pattern Anal Mach Intell 43(8):2634–2646

    Google Scholar 

  37. Hu M, Chen S (2019) Doubly aligned incomplete multi-view clustering. In: Twenty-Seventh International Joint Conference on Artificial Intelligence, pp 2262–2268

  38. Huang Z, Hu P, Zhou JT, Lv J, Peng X (2020) Partially view-aligned clustering. NeurIPS

  39. Kuhn HW (1955) The Hungarian method for the assignment problem. Naval Res Logistics Quart 2(1):83–97

    Article  MathSciNet  MATH  Google Scholar 

  40. Zhang C, Hu Q, Fu H, Zhu P, Cao X (2017) Latent multi-view subspace clustering. Comput Vis Pattern Recogn (CVPR) 4333–4341. https://doi.org/10.1109/CVPR.2017.461

  41. Zhao H, Ding Z, Fu Y (2017) Multi-view clustering via deep matrix factorization. In AAAI, pp 2921–2927

  42. Nie F, Li J, Li X (2017) Self-weighted multiview clustering with multiple graphs. In: Twenty-Sixth International Joint Conference on Artificial Intelligence, pp 2564–2570

  43. Yang M, Li Y, Huang Z, Liu Z, Hu P, Peng X (2021) Partially view-aligned representation learning with noise-robust contrastive loss. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 1134–1143

Download references

Acknowledgements

This work is sponsored by National Natural Science Foundation of China (CN) under Grant Numbers 62276164 and 61602296, ’Chenguang Program’ supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission under Grant Number 18CG54. Furthermore, this work is also supported by ’Science and technology innovation action plan’ Natural Science Foundation of Shanghai under Grant Number 22ZR1427000. The authors would like to thank their supports.

Author information

Authors and Affiliations

  1. College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, China

    Yanhao Zhang & Changming Zhu

Authors
  1. Yanhao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Changming Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Changming Zhu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Y., Zhu, C. Incomplete multi-view clustering via attention-based contrast learning. Int. J. Mach. Learn. & Cyber. 14, 4101–4117 (2023). https://doi.org/10.1007/s13042-023-01883-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-023-01883-w

Keywords

Search

Navigation