Robust Bayesian Max-Margin Clustering

We present max-margin Bayesian clustering (BMC), a general and robust frame-work that incorporates the max-margin criterion into Bayesian clustering models, as well as two concrete models of BMC to demonstrate its flexibility and effective-ness in dealing with different clustering tasks. The Dirichlet process max-margin Gaussian mixture is a nonparametric Bayesian clustering model that relaxes the underlying Gaussian assumption of Dirichlet process Gaussian mixtures by in-corporating max-margin posterior constraints, and is able to infer the number of clusters from data. We further extend the ideas to present max-margin cluster-ing topic model, which can learn the latent topic representation of each document while at the same time cluster documents in the max-margin fashion. Extensive experiments are performed on a number of real datasets, and the results indicate superior clustering performance of our methods compared to related baselines.