Abstract
This chapter introduces three kinds of multiview clustering methods. We begin with the multiview spectral clustering, where the clustering is carried out through the partition of a relationship graph of the data. It depends on the eigenvector of the adjacent matrix of the data. Then we consider the multiview subspace clustering, which aims at recovering the underlying subspace of the multiview data and performs clustering on it. Finally, we introduce distributed multiview clustering, which first learns the patterns from each view individually and then combines them together to learn optimal patterns for clustering, and multiview clustering ensemble. It combines the results of multiple clustering algorithms to obtain better performance. We also briefly introduce some other methods at the end of this chapter.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Achlioptas D, McSherry F (2005) On spectral learning of mixtures of distributions. In: Proceedings of the 18th annual conference on learning theory. ACM, Berlin, pp 458–469
Bolte J, Sabach S, Teboulle M (2014) Proximal alternating linearized minimization or nonconvex and nonsmooth problems. Math Program 146(1–2):459–494
Cai X, Nie F, Huang H (2013) Multi-view k-means clustering on big data
Chaudhuri K, Kakade SM, Livescu K, Sridharan K (2009) Multi-view clustering via canonical correlation analysis. In: Proceedings of the 26th annual international conference on machine learning. ACM, pp 129–136
Ding C, He X, Simon HD (2005) On the equivalence of nonnegative matrix factorization and spectral clustering. In: Proceedings of the 5th SIAM international conference on data mining, SIAM, pp 606–610
Elhamifar E, Vidal R (2013) Sparse subspace clustering: algorithm, theory, and applications. IEEE Trans Pattern Anal Mach Intell 35(11):2765–2781
Gao H, Nie F, Li X, Huang H (2015) Multi-view subspace clustering. In: 2015 IEEE international conference on computer vision, pp 4238–4246
Kumar A, Daum H (2011) A co-training approach for multi-view spectral clustering. In: Proceedings of the 28th international conference on machine learning. ACM, pp 393–400
Kumar A, Rai P, Daume H (2011) Co-regularized multi-view spectral clustering. Adv Neural Inf Process Syst 24:1413–1421
Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788
Liu J, Wang C, Gao J, Han J (2013) Multi-view clustering via joint nonnegative matrix factorization. In: Proceedings of the 13th SIAM international conference on data mining, SIAM, pp 252–260
Long B, Yu PS, Zhang ZM (2008) A general model for multiple view unsupervised learning. In: Proceedings of the 8th SIAM international conference on data mining, SIAM, pp 822–833
Nie F, Zeng Z, Tsang IW, Xu D, Zhang C (2011) Spectral embedded clustering: a framework for in-sample and out-of-sample spectral clustering. IEEE Trans Neural Netw 22(11):1796–1808
Salakhutdinov R, Roweis ST (2003) Adaptive overrelaxed bound optimization methods. In: Proceedings of the 20th international conference on machine learning, pp 664–671
Sun J, Lu J, Xu T, Bi J (2015) Multi-view sparse co-clustering via proximal alternating linearized minimization. In: Proceedings of the 32th international conference on machine learning, pp 757–766
Topchy A, Jain AK, Punch W (2005) Clustering ensembles: models of consensus and weak partitions. IEEE Trans Pattern Anal Mach Intell 27(12):1866–1881
Von Luxburg U (2007) A tutorial on spectral clustering. Stat Comput 17(4):395–416
Wang H, Nie F, Huang H, Risacher SL, Saykin AJ, Shen L, Initiative ADN (2012) Identifying disease sensitive and quantitative trait-relevant biomarkers from multidimensional heterogeneous imaging genetics data via sparse multimodal multitask learning. Bioinformatics 28(12):i127–i136
Wang H, Nie F, Huang H (2013a) Multi-view clustering and feature learning via structured sparsity. In: Proceedings of the 30th international conference on machine learning, pp 352–360
Wang H, Nie F, Huang H, Ding C (2013b) Heterogeneous visual features fusion via sparse multimodal machine. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3097–3102
Xie X, Sun S (2013) Multi-view clustering ensembles. In: Proceedings of the 5th international conference on machine learning and cybernetics, vol 1, pp 51–56
Zhou ZH, Tang W (2006) Clusterer ensemble. Knowl-Based Syst 19(1):77–83
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Sun, S., Mao, L., Dong, Z., Wu, L. (2019). Multiview Clustering. In: Multiview Machine Learning. Springer, Singapore. https://doi.org/10.1007/978-981-13-3029-2_5
Download citation
DOI: https://doi.org/10.1007/978-981-13-3029-2_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-3028-5
Online ISBN: 978-981-13-3029-2
eBook Packages: Computer ScienceComputer Science (R0)