Discriminative Bayesian Nonparametric Clustering

Discriminative Bayesian Nonparametric Clustering

Vu Nguyen, Dinh Phung, Trung Le, Hung Bui

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 2550-2556. https://doi.org/10.24963/ijcai.2017/355

We propose a general framework for discriminative Bayesian nonparametric clustering to promote the inter-discrimination among the learned clusters in a fully Bayesian nonparametric (BNP) manner. Our method combines existing BNP clustering and discriminative models by enforcing latent cluster indices to be consistent with the predicted labels resulted from probabilistic discriminative model. This formulation results in a well-defined generative process wherein we can use either logistic regression or SVM for discrimination. Using the proposed framework, we develop two novel discriminative BNP variants: the discriminative Dirichlet process mixtures, and the discriminative-state infinite HMMs for sequential data. We develop efficient data-augmentation Gibbs samplers for posterior inference. Extensive experiments in image clustering and dynamic location clustering demonstrate that by encouraging discrimination between induced clusters, our model enhances the quality of clustering in comparison with the traditional generative BNP models.
Keywords:
Machine Learning: Learning Graphical Models
Machine Learning: Unsupervised Learning