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Robust Bayesian Max-Margin Clustering
Changyou Chen · Jun Zhu · Xinhua Zhang

Mon Dec 08 04:00 PM -- 08:59 PM (PST) @ Level 2, room 210D #None

We present max-margin Bayesian clustering (BMC), a general and robust framework that incorporates the max-margin criterion into Bayesian clustering models, as well as two concrete models of BMC to demonstrate its flexibility and effectiveness 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 incorporating max-margin posterior constraints, and is able to infer the number of clusters from data. We further extend the ideas to present max-margin clustering 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.

Author Information

Changyou Chen (University at Buffalo)
Jun Zhu (Tsinghua University)
Xinhua Zhang (University of Illinois at Chicago (UIC))

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