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Multi-view Proximity Learning for Clustering

  • Kun-Yu Lin
  • Ling Huang
  • Chang-Dong WangEmail author
  • Hong-Yang Chao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10828)

Abstract

In recent years, multi-view clustering has become a hot research topic due to the increasing amount of multi-view data. Among existing multi-view clustering methods, proximity-based method is a typical class and achieves much success. Usually, these methods need proximity matrices as inputs, which can be constructed by some nearest-neighbors-based approaches. However, in this way, neither the intra-view cluster structure nor the inter-view correlation is considered in constructing proximity matrices. To address this issue, we propose a novel method, named multi-view proximity learning. By introducing the idea of representative, our model can consider both the relations between data objects and the cluster structure within individual views. Besides, the spectral-embedding-based scheme is adopted for modeling the correlations across different views, i.e. the view consistency and complement properties. Extensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of our method.

Keywords

Multi-view clustering Proximity learning Representative Spectral embedding 

Notes

Acknowledgments

This work was supported by NSFC (61502543), Guangdong Natural Science Funds for Distinguished Young Scholar (2016A030306014), and Tip-top Scientific and Technical Innovative Youth Talents of Guangdong special support program (2016TQ03X542).

References

  1. 1.
    Gao, Y., Gu, S., Li, J., Liao, Z.: The multi-view information bottleneck clustering. In: Kotagiri, R., Krishna, P.R., Mohania, M., Nantajeewarawat, E. (eds.) DASFAA 2007. LNCS, vol. 4443, pp. 912–917. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-71703-4_78CrossRefGoogle Scholar
  2. 2.
    Müller, E., Assent, I., Sánchez, I.P., Mülle, Y., Böhm, K.: Outlier ranking via subspace analysis in multiple views of the data. In: 12th IEEE International Conference on Data Mining, pp. 529–538. IEEE (2012)Google Scholar
  3. 3.
    Chen, C., He, J., Bliss, N., Tong, H.: Towards optimal connectivity on multi-layered networks. IEEE Trans. Knowl. Data Eng. 29(10), 2332–2346 (2017)CrossRefGoogle Scholar
  4. 4.
    Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the 11th Annual Conference on Computational Learning Theory, pp. 92–100 (1998)Google Scholar
  5. 5.
    Amini, M., Usunier, N., Goutte, C.: Learning from multiple partially observed views-an application to multilingual text categorization. Adv. Neural Inf. Process. Syst. 22, 28–36 (2009)Google Scholar
  6. 6.
    Xu, Y.M., Wang, C.D., Lai, J.H.: Weighted multi-view clustering with feature selection. Pattern Recogn. 53, 25–35 (2016)CrossRefGoogle Scholar
  7. 7.
    Zhang, G.Y., Wang, C.D., Huang, D., Zheng, W.S.: Multi-view collaborative locally adaptive clustering with Minkowski metric. Expert Syst. Appl. 86, 307–320 (2017)CrossRefGoogle Scholar
  8. 8.
    Huang, L., Chao, H.Y., Wang, C.D.: Multi-view intact space clustering. In: Proceedings of the 4th Asian Conference on Pattern Recognition, pp. 500–505 (2017)Google Scholar
  9. 9.
    Xu, R., Wunsch, D.C.: Survey of clustering algorithms. IEEE Trans. Neural Netw. 16(3), 645–678 (2005)CrossRefGoogle Scholar
  10. 10.
    Xu, C., Tao, D., Xu, C.: A survey on multi-view learning. CoRR abs/1304.5634 (2013)Google Scholar
  11. 11.
    Xia, T., Tao, D., Mei, T., Zhang, Y.: Multiview spectral embedding. IEEE Trans. Syst. Man Cybern. Part B 40(6), 1438–1446 (2010)CrossRefGoogle Scholar
  12. 12.
    Tzortzis, G., Likas, A.: Kernel-based weighted multi-view clustering. In: 12th IEEE International Conference on Data Mining, pp. 675–684 (2012)Google Scholar
  13. 13.
    Kumar, A., Daumé, H.: A co-training approach for multi-view spectral clustering. In: Proceedings of the 28th International Conference on Machine Learning, pp. 393–400 (2011)Google Scholar
  14. 14.
    Son, J.W., Jeon, J., Lee, A., Kim, S.J.: Spectral clustering with brainstorming process for multi-view data. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence, pp. 2548–2554 (2017)Google Scholar
  15. 15.
    Kumar, A., Rai, P., Daumé, H.: Co-regularized multi-view spectral clustering. In: Advances in Neural Information Processing Systems, pp. 1413–1421 (2011)Google Scholar
  16. 16.
    Lu, C., Yan, S., Lin, Z.: Convex sparse spectral clustering: single-view to multi-view. IEEE Trans. Image Process. 25(6), 2833–2843 (2016)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Wang, C.D., Lai, J.H., Yu, P.: Multi-view clustering based on belief propagation. IEEE Trans. Knowl. Data Eng. 28(4), 1007–1021 (2016)CrossRefGoogle Scholar
  18. 18.
    Xia, R., Pan, Y., Du, L., Yin, J.: Robust multi-view spectral clustering via low-rank and sparse decomposition. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence, pp. 2149–2155 (2014)Google Scholar
  19. 19.
    Luxburg, U.V.: A tutorial on spectral clustering. Stat. Comput. 17(4), 395–416 (2007)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Zelnik-Manor, L., Perona, P.: Self-tuning spectral clustering. Adv. Neural Inf. Process. Syst. 17, 1601–1608 (2005)Google Scholar
  21. 21.
    Shah, S.A., Koltun, V.: Robust continuous clustering. Proc. Nat. Acad. Sci. U.S.A. 114(37), 9814 (2017)CrossRefGoogle Scholar
  22. 22.
    Nie, F., Wang, X., Huang, H.: Clustering and projected clustering with adaptive neighbors. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 977–986 (2014)Google Scholar
  23. 23.
    Wang, W., Carreira-Perpinán, M.A.: Projection onto the probability simplex: an efficient algorithm with a simple proof, and an application. CoRR abs/1309.1541 (2013)Google Scholar
  24. 24.
    Bache, K., Lichman, M.: UCI machine learning repository (2013). http://archive.ics.uci.edu/ml/index.php
  25. 25.
    Li, F.F., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2004, p. 178 (2004)Google Scholar
  26. 26.
    Li, Y., Nie, F., Huang, H., Huang, J.: Large-scale multi-view spectral clustering via bipartite graph. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence, pp. 2750–2756 (2015)Google Scholar
  27. 27.
    MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297 (1967)Google Scholar
  28. 28.
    Cai, X., Nie, F., Huang, H.: Multi-view \(k\)-means clustering on big data. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence, pp. 2598–2604 (2013)Google Scholar
  29. 29.
    Nie, F., Cai, G., Li, X.: Multi-view clustering and semi-supervised classification with adaptive neighbours. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence, pp. 2408–2414 (2017)Google Scholar
  30. 30.
    Nie, F., Wang, X., Jordan, M.I., Huang, H.: The constrained Laplacian rank algorithm for graph-based clustering. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence, pp. 1969–1976 (2016)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Kun-Yu Lin
    • 1
  • Ling Huang
    • 1
  • Chang-Dong Wang
    • 1
    Email author
  • Hong-Yang Chao
    • 1
  1. 1.School of Data and Computer ScienceSun Yat-sen UniversityGuangzhouChina

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