Learning a Bi-Stochastic Data Similarity Matrix

Abstract

An idealized clustering algorithm seeks to learn a cluster-adjacency matrix such that, if two data points belong to the same cluster, the corresponding entry would be 1, otherwise the entry would be 0. This integer (1/0) constraint makes it difficult to find the optimal solution. We propose a relaxation on the cluster-adjacency matrix, by deriving a bi-stochastic matrix from a data similarity (e.g., kernel) matrix according to the Bregman divergence. Our general method is named the {\em Bregmanian Bi-Stochastication} (BBS) algorithm. We focus on two popular choices of the Bregman divergence: the Euclidian distance and the KL divergence. Interestingly, the BBS algorithm using the KL divergence is equivalent to the Sinkhorn-Knopp (SK) algorithm for deriving bi-stochastic matrices. We show that the BBS algorithm using the Euclidian distance is closely related to the relaxed $k$-means clustering and can often produce noticeably superior clustering results than the SK algorithm (and other algorithms such as Normalized Cut), through extensive experiments on public data sets.