Nguyen, Vu, Phung, Dinh, Le, Trung and Bui, Hung 2017, Discriminative Bayesian nonparametric clustering, in IJCAI 2017 : Proceedings of the 26th International Joint Conference on Artificial Intelligence, AAAI Press, Morgantown, W. Va., pp. 2550-2556.
IJCAI 2017 : Proceedings of the 26th International Joint Conference on Artificial Intelligence
Publication date
2017
Conference series
International Joint Conference on Artificial Intelligence
Start page
2550
End page
2556
Total pages
7
Publisher
AAAI Press
Place of publication
Morgantown, W. Va.
Summary
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.
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