Following the basic principles of Information-Theoretic Learning (ITL), in this paper we propose Minimum Entropy Encoders (MEEs), a novel approach to data clustering. We consider a set of functions that project each input point onto a minimum entropy configuration (code). The encoding functions are modeled by kernel machines and the resulting code collects the cluster membership probabilities. Two regularizers are included to balance the distribution of the output features and favor smooth solutions, respectively, thus leading to an unconstrained optimization problem that can be efficiently solved by conjugate gradient or concave-convex procedures. The relationships with Maximum Margin Clustering algorithms are investigated, which show that MEEs overcomes some of the critical issues, such as the lack of a multi-class extension and the need to face problems with a large number of constraints. A massive evaluation on several benchmarks of the proposed approach shows improvements over state-of-the-art techniques, both in terms of accuracy and computational complexity.
['Stefano Melacci', 'Marco Gori']
Kernel Methods for Minimum Entropy Encoding