The multi-view information bottleneck clustering

Authors:
Yan Gao;Shiwen Gu;Jianhua Li;Zhining Liao
Affiliations:
College of Information Science and Engineering, Central South University, Hunan, China;College of Information Science and Engineering, Central South University, Hunan, China;College of Information Science and Engineering, Central South University, Hunan, China;Department of Computer Science, Loughborough University, Leics, UK
Venue:
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Year:
2007

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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 realworld datasets indicate that our algorithm considering the relationship among multiple views can provide solution with improved quality in multiview setting.