Abstract
In this paper, we propose a new algorithm for information bottleneck method in multi-view setting where instances have multiple independent representations. By introducing the two important conditions, conditional independence and compatibility, into the information bottleneck clustering, the compatible constraint maximizing the agreement between clustering hypotheses on different views is imposed on the individual views to cluster instances. Our algorithm is developed by the compatible constraint. Experiments on three real-world datasets indicate that our algorithm considering the relationship among multiple views can provide solution with improved quality in multi-view setting.
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References
Slonim, N.: The Information Bottleneck: Theory and Applications. PhD thesis, Hebrew University, Jerusalem, Israel (2002)
Slonim, N., Friedman, N., Tishby, N.: Unsupervised Document Classification using Information Maximization. In: Proc. 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Tampere, Finland, pp. 129–136 (2002)
Goldberger, J., Greenspan, H., Gordon, S.: Unsupervised Image Clustering Using the Information Bottleneck Method. In: Van Gool, L. (ed.) DAGM 2002. LNCS, vol. 2449, pp. 158–165. Springer, Heidelberg (2002)
Dhillon, I.S., Mallela, S., Modha, D.S.: Information-theoretic co-clustering. In: Proceedings of The Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 89–98 (2003)
Bekkerman, R., El-Yaniv, R., McCallum, A.: Multi-Way Distributional Clustering via Pairwise Interactions. In: Proceedings of the 22 International Conference on Machine Learning, Bonn, Germany, pp. 41–48 (2005)
Gondek, D., Hofmann, T.: Non-redundant data clustering. In: Proceedings of the Fourth IEEE International Conference on Data Mining, Brighton, UK, pp. 75–82 (2004)
Gondek, D., Hofmann, T.: Non-Redundant Clustering with Conditional Ensembles. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, Illinois, USA, pp. 70–77 (2005)
Topchy, A., Jain, A.K., Punch, W.: Combining multiple weak clusterings. In: Processings of the Third IEEE International Conference on Data Mining, Florida, USA, pp. 331–338 (2003)
Strehl, A., Ghosh, J.: Cluster Ensembles – A Knowledge Reuse Framework for Combining Multiple Partitions. Journal on Machine Learning Research 3, 583–617 (2002)
Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Annual Conference on Computational Learning Theory (COLT-98), Madison, USA, pp. 92–100 (1998)
Cover, T.M., Thomas, J.A.: The elements of information theory. China Machine Press, Bejing (2005)
Co-training, Webkb, newsgroup20, http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-20
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Gao, Y., Gu, S., Li, J., Liao, Z. (2007). The Multi-view Information Bottleneck Clustering. In: Kotagiri, R., Krishna, P.R., Mohania, M., Nantajeewarawat, E. (eds) Advances in Databases: Concepts, Systems and Applications. DASFAA 2007. Lecture Notes in Computer Science, vol 4443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71703-4_78
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DOI: https://doi.org/10.1007/978-3-540-71703-4_78
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71702-7
Online ISBN: 978-3-540-71703-4
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