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Learning a Bi-Stochastic Data Similarity Matrix

Fei Wang, Ping Li, and Arnd Christian König


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 olution. 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 Bregmanian Bi-Stochastication (BBS) algorithm. We focus on two popular choices of the Bregman divergence: the Euclidian distance and he 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 lgorithm 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.


Publication typeInproceedings
Published inThe 10th International Conference on Data Mining (ICDM)
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