Skip to main content
SpringerLink
Log in

Consensus Graph Learning for Incomplete Multi-view Clustering

  • Conference paper
  • First Online:
Advances in Knowledge Discovery and Data Mining (PAKDD 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11439))

Included in the following conference series:

Abstract

Multi-view data clustering is a fundamental task in current machine learning, known as multi-view clustering. Existing multi-view clustering methods mostly assume that each data instance is sampled in all views. However, in real-world applications, it is common that certain views miss number of data instances, resulting in incomplete multi-view data. This paper concerns the task of clustering of incomplete multi-view data. We propose a novel Graph-based Incomplete Multi-view Clustering (GIMC) to perform this task. GIMC can effectively construct a complete graph for each view with the help of other view(s), and automatically weight each constructed graph to learn a consensus graph, which gives the final clusters. An alternating iterative optimization algorithm is proposed to optimize the objective function. Experimental results on real-world datasets show that the proposed method outperforms state-of-the-art baseline methods markedly.

W. Zhou and H. Wang—Authors contributed equally to this work.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Chao, G., Sun, S., Bi, J.: A survey on multi-view clustering. CoRR abs/1712.06246 (2017)

    Google Scholar 

  2. Dhillon, I.S.: Co-clustering documents and words using bipartite spectral graph partitioning. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 269–274 (2001)

    Google Scholar 

  3. Eaton, E., Desjardins, M., Jacob, S.: Multi-view constrained clustering with an incomplete mapping between views. Knowl. Inf. Syst. 38(1), 231–257 (2014)

    Article  Google Scholar 

  4. Fan, K.: On a theorem of Weyl concerning eigenvalues of linear transformations I. Proc. Natl. Acad. Sci. U. S. A. 35(11), 652–655 (1949)

    Article  MathSciNet  Google Scholar 

  5. Hou, C., Nie, F., Tao, H., Yi, D.: Multi-view unsupervised feature selection with adaptive similarity and view weight. IEEE Trans. Knowl. Data Eng. 29(9), 1998–2011 (2017)

    Article  Google Scholar 

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

    Google Scholar 

  7. Lemaréchal, C., Boyd, S., Vandenberghe, L.: Convex optimization. Eur. J. Oper. Res. 170(1), 326–327 (2006)

    Article  Google Scholar 

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

    Google Scholar 

  9. Liu, J., Wang, C., Gao, J., Han, J.: Multi-view clustering via joint nonnegative matrix factorization. In: Proceedings of the SIAM International Conference on Data Mining, pp. 252–260 (2013)

    Google Scholar 

  10. Liu, J., Jiang, Y., Li, Z., Zhou, Z.H., Lu, H.: Partially shared latent factor learning with multiview data. IEEE Trans. Neural Netw. Learn. Syst. 26(6), 1233–1246 (2015)

    Article  MathSciNet  Google Scholar 

  11. Liu, X., et al.: Late fusion incomplete multi-view clustering. IEEE Trans. Pattern Anal. Mach. Intell. 1 (2018). https://doi.org/10.1109/TPAMI.2018.2879108

  12. Liu, X., et al.: Optimal neighborhood kernel clustering with multiple kernels. In: Proceedings of the AAAI International Conference on Artificial Intelligence, pp. 2266–2272 (2017)

    Google Scholar 

  13. Mohar, B., Alavi, Y., Chartrand, G., Oellermann, O.: The Laplacian spectrum of graphs. Graph Theory Comb. Appl. 2(12), 871–898 (1991)

    MathSciNet  Google Scholar 

  14. Nie, F., Cai, G., Li, J., Li, X.: Auto-weighted multi-view learning for image clustering and semi-supervised classification. IEEE Trans. Image Process. 27(3), 1501–1511 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  15. Nie, F., Li, J., Li, X.: Parameter-free auto-weighted multiple graph learning: a framework for multiview clustering and semi-supervised classification. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 1881–1887 (2016)

    Google Scholar 

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

    Google Scholar 

  17. Nie, F., Wang, X., Jordan, M.I., Huang, H.: The constrained Laplacian rank algorithm for graph-based clustering. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 1969–1976 (2016)

    Google Scholar 

  18. Saha, M.: A graph based approach to multiview clustering. In: Maji, P., Ghosh, A., Murty, M.N., Ghosh, K., Pal, S.K. (eds.) PReMI 2013. LNCS, vol. 8251, pp. 128–133. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-45062-4_17

    Chapter  Google Scholar 

  19. Shao, W., He, L., Lu, C., Yu, P.S.: Online multi-view clustering with incomplete views. In: Proceedings of the 2016 IEEE International Conference on Big Data, pp. 1012–1017 (2016)

    Google Scholar 

  20. Shao, W., He, L., Yu, P.S.: Clustering on multi-source incomplete data via tensor modeling and factorization. In: Cao, T., Lim, E.-P., Zhou, Z.-H., Ho, T.-B., Cheung, D., Motoda, H. (eds.) PAKDD 2015. LNCS (LNAI), vol. 9078, pp. 485–497. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18032-8_38

    Chapter  Google Scholar 

  21. Shao, W., He, L., Yu, P.S.: Multiple incomplete views clustering via weighted nonnegative matrix factorization with \(L_{2,1}\) regularization. In: Appice, A., Rodrigues, P.P., Santos Costa, V., Soares, C., Gama, J., Jorge, A. (eds.) ECML PKDD 2015. LNCS (LNAI), vol. 9284, pp. 318–334. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23528-8_20

    Chapter  Google Scholar 

  22. Tao, H., Hou, C., Liu, X., Liu, T., Yi, D., Zhu, J.: Reliable multi-view clustering. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 4123–4130 (2018)

    Google Scholar 

  23. Tao, H., Hou, C., Zhu, J., Yi, D.: Multi-view clustering with adaptively learned graph. In: Proceedings of the Asian Conference on Machine Learning, pp. 113–128 (2017)

    Google Scholar 

  24. Wang, H., Yang, Y., Li, T.: Multi-view clustering via concept factorization with local manifold regularization. In: Proceedings of the IEEE International Conference on Data Mining, pp. 1245–1250 (2016)

    Google Scholar 

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

    Article  Google Scholar 

  26. Xu, C., Tao, D., Xu, C.: Multi-view learning with incomplete views. IEEE Trans. Image Process. 24(12), 5812–5825 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  27. Yang, Y., Wang, H.: Multi-view clustering: a survey. Big Data Min. Anal. 1(2), 83–107 (2018)

    Article  Google Scholar 

  28. Yang, Y., Zhan, D.C., Sheng, X.R., Jiang, Y.: Semi-supervised multi-modal learning with incomplete modalities. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 2998–3004 (2018)

    Google Scholar 

  29. Yin, Q., Wu, S., Wang, L.: Incomplete multi-view clustering via subspace learning. In: Proceedings of the ACM International on Conference on Information and Knowledge Management, pp. 383–392 (2015)

    Google Scholar 

  30. Yin, Q., Wu, S., Wang, L.: Unified subspace learning for incomplete and unlabeled multi-view data. Pattern Recognit. 67, 313–327 (2017)

    Article  Google Scholar 

  31. Zhang, X., Zong, L., Liu, X., Yu, H.: Constrained NMF-based multi-view clustering on unmapped data. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 3174–3180 (2015)

    Google Scholar 

  32. Zhang, Y., Yang, Y., Li, T., Fujita, H.: A multitask multiview clustering algorithm in heterogeneous situations based on LLE and LE. Knowl.-Based Syst. 163, 776–786 (2019)

    Article  Google Scholar 

  33. Zhao, H., Liu, H., Fu, Y.: Incomplete multi-modal visual data grouping. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 2392–2398 (2016)

    Google Scholar 

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

    Article  Google Scholar 

  35. Zong, L., Zhang, X., Liu, X., Yu, H.: Weighted multi-view spectral clustering based on spectral perturbation. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 4621–4628 (2018)

    Google Scholar 

Download references

Acknowledgment

This work was supported by the National Natural Science Foundation of China (No. 61572407). Working at the University of Illinois at Chicago supported by the China Scholarship Council (No. 20170700064) has also given Hao Wang a broader perspective on data mining and machine learning.

Author information

Authors and Affiliations

  1. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China

    Wei Zhou, Hao Wang & Yan Yang

Authors
  1. Wei Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hao Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yan Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yan Yang .

Editor information

Editors and Affiliations

  1. Hong Kong University of Science and Technology, Hong Kong, China

    Qiang Yang

  2. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  3. University of Macau, Taipa, Macau, China

    Zhiguo Gong

  4. Southeast University, Nanjing, China

    Min-Ling Zhang

  5. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Sheng-Jun Huang

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhou, W., Wang, H., Yang, Y. (2019). Consensus Graph Learning for Incomplete Multi-view Clustering. In: Yang, Q., Zhou, ZH., Gong, Z., Zhang, ML., Huang, SJ. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science(), vol 11439. Springer, Cham. https://doi.org/10.1007/978-3-030-16148-4_41

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-16148-4_41

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-16147-7

  • Online ISBN: 978-3-030-16148-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation