PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Identifying structure across pre-partitioned data
Zvika Marx, Ido Dagan and Eli Shamir
In: Advances in Neural Information Processing Systems 16 (NIPS 2003), 8-13 Dec 2003, Vancouver, Canada.

Abstract

We propose an information-theoretic clustering approach that incorporates a pre-known partition of the data, aiming to identify common clusters that cut across the given partition. In the standard clustering setting the formation of clusters is guided by a single source of feature information. The newly utilized pre-partition factor introduces an additional bias that counterbalances the impact of the features whenever they become correlated with this known partition. The resulting algorithmic framework was applied successfully to synthetic data, as well as to identifying text-based cross-religion correspondences.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Natural Language Processing
Information Retrieval & Textual Information Access
ID Code:796
Deposited By:Ido Dagan
Deposited On:30 December 2004