Kernelized Infomax Clustering

Part of Advances in Neural Information Processing Systems 18 (NIPS 2005)

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David Barber, Felix Agakov


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 makes it potentially attractive for clustering large data sets.