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Summary: Kernelized Infomax Clustering
Felix V. Agakov
Edinburgh University
Edinburgh EH1 2QL, U.K.
felixa@inf.ed.ac.uk
David Barber
IDIAP Research Institute
CH-1920 Martigny Switzerland
david.barber@idiap.ch
Abstract
We propose a simple information-theoretic approach to soft clus-
tering based on maximizing the mutual information I(x, y) between
the unknown cluster labels y and the training patterns x with re-
spect to parameters of specifically constrained encoding distribu-
tions. 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 learn-
ing parameters of the kernelized encoder. The procedure does not
require computations of eigenvalues of the Gram matrices, which
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