PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Kernelized Infomax Clustering
Felix Agakov and David Barber
In: NIPS 2005, 5-8 Dec 2005, Vancouver, Canada.


We propose a simple information-theoretic approach to soft clustering based on maximizing the mutual information $I(x,y)$ between the unknown cluster labels $y$ and the training patterns $x$ with respect to parameters of specifically constrained encoding distributions. The constraints are chosen such that patterns are likely to be clustered similarly if they lie close to specific unknown vectors in the feature space. The method may be conveniently applied to learning the optimal affinity matrix, which corresponds to learning parameters of the kernelized encoder. The procedure does not require computations of eigenvalues of the Gram matrices, which makes it potentially attractive for clustering large data sets.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
ID Code:1196
Deposited By:Felix Agakov
Deposited On:28 November 2005