Abstract
Data with multiple representations (views) arise naturally in many applications and multi-view algorithms can substantially improve the classification and clustering results. In this work, we study the problem of multi-view clustering and propose a multi-view convex mixture model that locates exemplars (cluster representatives) in the dataset by simultaneously considering all views. Convex mixture models are simplified mixture models that exhibit several attractive characteristics. The proposed algorithm extends the single view convex mixture models so as to handle data with any number of representations, taking into account the diversity of the views while preserving their good properties. Empirical evaluations on synthetic and real data demonstrate the effectiveness and potential of our method.
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
Preview
Unable to display preview. Download preview PDF.
References
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)
Muslea, I., Minton, S., Knoblock, C.A.: Active + semi-supervised learning = robust multi-view learning. In: Proceedings of the 19th International Conference on Machine Learning, pp. 435–442 (2002)
Brefeld, U., Scheffer, T.: Co-em support vector learning. In: Proceedings of the 21st International Conference on Machine Learning (2004)
Bickel, S., Scheffer, T.: Multi-view clustering. In: Proceedings of the 4th IEEE International Conference on Data Mining, pp. 19–26 (2004)
de Sa, V.R.: Spectral clustering with two views. In: Proceedings of the 22nd International Conference on Machine Learning Workshop on Learning with Multiple Views, pp. 20–27 (2005)
Zhou, D., Burges, C.J.C.: Spectral clustering and transductive learning with multiple views. In: Proceedings of the 24th International Conference on Machine Learning, pp. 1159–1166 (2007)
Long, B., Yu, P.S., Zhang, Z.M.: A general model for multiple view unsupervised learning. In: Proceedings of the 2008 SIAM International Conference on Data Mining, pp. 822–833 (2008)
Nigam, K., Ghani, R.: Analyzing the effectiveness and applicability of co-training. In: Proceedings of the 9th International Conference on Information and Knowledge Management, pp. 86–93 (2000)
Bickel, S., Scheffer, T.: Estimation of mixture models using co-em. In: Proceedings of the 16th European Conference on Machine Learning, pp. 35–46 (2005)
Lashkari, D., Golland, P.: Convex clustering with exemplar-based models. In: Advances in Neural Information Processing Systems, vol. 20, pp. 825–832 (2008)
Banerjee, A., Merugu, S., Dhillon, I.S., Ghosh, J.: Clustering with Bregman divergences. J. Machine Learning Research 6, 1705–1749 (2005)
Csiszár, I., Shields, P.C.: Information theory and statistics: A tutorial. Communications and Information Theory 1(4), 417–528 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tzortzis, G., Likas, A. (2009). Convex Mixture Models for Multi-view Clustering. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5769. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04277-5_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-04277-5_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04276-8
Online ISBN: 978-3-642-04277-5
eBook Packages: Computer ScienceComputer Science (R0)