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In this paper, we present a local information theoretic approach toexplicitly learn probabilistic clustering of a discrete random variable. Ourformulation yields a convex maximization problem for which it is NP-hard tofind the global optimum. In order to algorithmically solve this optimizationproblem, we propose two relaxations that are solved via gradient ascent andalternating maximization. Experiments on the MSR Sentence Completion Challenge,MovieLens 100K, and Reuters21578 datasets demonstrate that our approach iscompetitive with existing techniques and worthy of further investigation.

Probabilistic Clustering Using Maximal Matrix Norm Couplings