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Poster
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
in Poster Session and Reception »

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.

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Author Information

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

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