Probabilistic Clustering Using Maximal Matrix Norm Couplings
This work addresses clustering problems for data analysis, but it appears incremental as it builds on existing methods with competitive rather than groundbreaking results.
The paper tackles probabilistic clustering of discrete random variables by formulating it as a convex maximization problem that is NP-hard, proposing two relaxations solved via gradient ascent and alternating maximization. Experiments on datasets like MSR Sentence Completion Challenge and MovieLens 100K show the approach is competitive with existing techniques.
In this paper, we present a local information theoretic approach to explicitly learn probabilistic clustering of a discrete random variable. Our formulation yields a convex maximization problem for which it is NP-hard to find the global optimum. In order to algorithmically solve this optimization problem, we propose two relaxations that are solved via gradient ascent and alternating maximization. Experiments on the MSR Sentence Completion Challenge, MovieLens 100K, and Reuters21578 datasets demonstrate that our approach is competitive with existing techniques and worthy of further investigation.