Abstract
Multi-view clustering (MVC) has gained considerable attention recently. In this paper, we present a hybrid matrix factorization (HMF) framework which is a combination of the nonnegative factorization and the symmetric nonnegative matrix factorization for MVC. HMF can uncover linear and nonlinear manifold within multi-view dataset. In addition, HMF also learns weights for each view to characterize the contribution of each view to the final common clustering assignment. The proposed model can be solved by nonnegative least squares. Unlike previous approaches, our approach can obtain the clustering results straightforwardly due to the nonnegative constraints. We conduct experiments on multi-view benchmark datasets to verify the effectiveness of our proposed approach.
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References
Amini, M.R., 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)
Bickel, S., Scheffer, T.: Multi-view clustering. In: ICDM vol. 4, pp. 19–26 (2004)
Blaschko, M.B., Lampert, C.H.: Correlational spectral clustering. In: IEEE Conference on Computer Vision and Pattern Recognition, 2008. CVPR 2008, pp. 1–8. IEEE (2008)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)
Blum, A., Mitchell, T.M.: Combining labeled and unlabeled data with co-training. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory, pp. 92–100 (1998)
Cai, X., Nie, F., Huang, H.: Multi-view k-means clustering on big data. In: Proceedings of the Twenty-Third international joint conference on Artificial Intelligence, pp. 2598–2604. AAAI Press (2013)
Chaudhuri, K., Kakade, S.M., Livescu, K., Sridharan, K.: Multi-view clustering via canonical correlation analysis. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 129–136. ACM (2009)
Ding, C.H.Q., Li, T., Peng, W.: Nonnegative matrix factorization and probabilistic latent semantic indexing: equivalence, chi-square statistic, and a hybrid method. In: AAAI 2006 Proceedings of the 21st National Conference on Artificial Intelligence , vol. 1, pp. 342–347 (2006)
Du, R., Drake, B.L., Park, H.: Hybrid clustering based on content and connection structure using joint nonnegative matrix factorization. J. Global Optim. 74, 1–17 (2017)
Hubert, L., Arabie, P.: Comparing partitions. J. Classif. 2(1), 193–218 (1985). https://doi.org/10.1007/BF01908075
Jain, A., Murty, M., Flynn, P.: Data clustering: a review. ACM Comput. Surv. 31(3), 264–323 (1999)
Kim, H., Park, H.: Nonnegative matrix factorization based on alternating nonnegativity constrained least squares and active set method. SIAM J. Matrix Anal. Appl. 30(2), 713–730 (2008)
Kuang, D., Ding, C., Park, H.: Symmetric nonnegative matrix factorization for graph clustering. In: 12th SIAM International Conference on Data Mining, SDM 2012, pp. 106–117 (2012)
Kumar, A., Daumé, H.: A co-training approach for multi-view spectral clustering. In: Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 393–400 (2011)
Kumar, A., Rai, P., Daume, H.: Co-regularized multi-view spectral clustering. In: Advances in 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)
Liu, J., Wang, C., Gao, J., Han, J.: Multi-view clustering via joint nonnegative matrix factorization. In: Proceedings of SDM, vol. 13, pp. 252–260. SIAM (2013)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Manning, C.D., Raghavan, P., Schütze, H., et al.: Introduction to Information Retrieval, vol. 1. Cambridge University Press, Cambridge (2008)
Ng, A.Y., Jordan, M.I., Weiss, Y., et al.: On spectral clustering: Analysis and an algorithm. In: Advances in neural information processing systems, vol. 2, 849–856 (2002)
Rasiwasia, N., et al.: A new approach to cross-modal multimedia retrieval. In: Proceedings of the 18th ACM International Conference on Multimedia, pp. 251–260. ACM (2010)
de Sa, V.R.: Spectral clustering with two views. In: ICML Workshop on Learning with Multiple Views, pp. 20–27 (2005)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Patt. Anal. Mach. Intell. 22(8), 888–905 (2000)
Tang, W., Lu, Z., Dhillon, I.S.: Clustering with multiple graphs. In: Ninth IEEE International Conference on Data Mining, 2009. ICDM 2009, pp. 1016–1021. IEEE (2009)
Zhao, X., Evans, N., Dugelay, J.L.: A subspace co-training framework for multi-view clustering. Patt. Recogn. Lett. 41, 73–82 (2014)
Zhou, D., Burges, C.J.C.: Spectral clustering and transductive learning with multiple views. In: International Conference on Machine Learning (2007)
Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grants No. 61602248) and the Natural Science Foundation of Jiangsu Province (Grants No. BK20160741).
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Yu, H., Shu, X. (2019). Hybrid Matrix Factorization for Multi-view Clustering. In: Cui, Z., Pan, J., Zhang, S., Xiao, L., Yang, J. (eds) Intelligence Science and Big Data Engineering. Big Data and Machine Learning. IScIDE 2019. Lecture Notes in Computer Science(), vol 11936. Springer, Cham. https://doi.org/10.1007/978-3-030-36204-1_25
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