Probabilistic Clustering Using Maximal Matrix Norm Couplings
Machine Learning
2018-10-12 v1 Information Theory
math.IT
Machine Learning
Abstract
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.
Cite
@article{arxiv.1810.04738,
title = {Probabilistic Clustering Using Maximal Matrix Norm Couplings},
author = {David Qiu and Anuran Makur and Lizhong Zheng},
journal= {arXiv preprint arXiv:1810.04738},
year = {2018}
}
Comments
Presented at 56th Annual Allerton Conference on Communication, Control, and Computing, 2018