Abstract
In real applications, multi-view clustering with incomplete data has played an important role in the data mining field. How to design an algorithm to promote the clustering performance is a challenging problem. In this paper, we propose an approach with learned graph to handle the case that each view suffers from some missing information. It combines incomplete multi-view data and clusters it simultaneously by learning the ideal structures. For each view, with an initial input graph, it excavates a clustering structure with the consideration of consistency with the other views. The learned structured graphs have exactly c (the predefined number of clusters) connected components so that the clustering results can be obtained without requiring any post-clustering. An efficient optimization strategy is provided, which can simultaneously handle both the whole and the partial regularization problems. The proposed method exhibits impressive performance in experiments.
Supported by the National Natural Science Foundation of China (No. 61473302, 61503396).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bickel, S., Scheffer, T.: Multi-view clustering. In: Proceedings of the 4th IEEE International Conference on Data Mining (ICDM), pp. 19–26 (2004)
Brand, M.: Incremental singular value decomposition of uncertain data with missing values. In: Computer Vision - ECCV 7th European Conference on Computer Vision, pp. 707–720 (2002)
Cheng, W., Zhang, X., Guo, Z., Wu, Y., Sullivan, P.F., Wang, W.: Flexible and robust co-regularized multi-domain graph clustering. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 320–328 (2013)
Chung, F.R.: Spectral Graph Theory, vol. 92. American Mathematical Society, New York (1997)
Fan, K.: On a theorem of Weyl concerning eigenvalues of linear transformations i. Proc. Natl. Acad. Sci. 35(11), 652–655 (1949)
Greene, D., Cunningham, P.: A matrix factorization approach for integrating multiple data views. In: Proceedings of Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2009, pp. 423–438 (2009)
Guo, Y.: Convex subspace representation learning from multi-view data. In: Proceedings of the 27th AAAI Conference on Artificial Intelligence (2013)
Huang, J., Nie, F., Huang, H.: A new simplex sparse learning model to measure data similarity for clustering. In: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence IJCAI, pp. 3569–3575 (2015)
Huang, J., Nie, F., Huang, H., Lei, Y., Ding, C.H.Q.: Social trust prediction using rank-k matrix recovery. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence IJCAI, pp. 2647–2653 (2013)
Kumar, A., Rai, P., Daume, H.: Co-regularized multi-view spectral clustering. In: Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems, pp. 1413–1421 (2011)
Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401(6755), 788–791 (1999)
Li, S., Jiang, Y., Zhou, Z.: Partial multi-view clustering. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence, pp. 1968–1974 (2014)
Lin, Z., Chen, M., Ma, Y.: The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices. arXiv preprint arXiv:1009.5055 (2010)
Liu, J., Wang, C., Gao, J., Han, J.: Multi-view clustering via joint nonnegative matrix factorization. In: Proceedings of the 2013 SIAM International Conference on Data Mining, pp. 252–260. SIAM (2013)
Mohar, B., Alavi, Y., Chartrand, G., Oellermann, O.: The laplacian spectrum of graphs. Graph theory, combinatorics, and applications 2(871–898), 12 (1991)
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)
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
Shao, W., Shi, X., Philip, S.Y.: Clustering on multiple incomplete datasets via collective kernel learning. In: 2013 IEEE 13rd International Conference on Data Mining (ICDM), pp. 1181–1186. IEEE (2013)
Wang, Q., Si, L., Shen, B.: Learning to hash on partial multi-modal data. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence, IJCAI, pp. 3904–3910 (2015)
Zhao, H., Liu, H., Fu, Y.: Incomplete multi-modal visual data grouping. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence, IJCAI, pp. 2392–2398 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Wu, J., Zhuge, W., Tao, H., Hou, C., Zhang, Z. (2018). Incomplete Multi-view Clustering via Structured Graph Learning. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11012. Springer, Cham. https://doi.org/10.1007/978-3-319-97304-3_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-97304-3_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-97303-6
Online ISBN: 978-3-319-97304-3
eBook Packages: Computer ScienceComputer Science (R0)