Version 2 2024-06-05, 04:36Version 2 2024-06-05, 04:36
Version 1 2017-11-03, 21:56Version 1 2017-11-03, 21:56
conference contribution
posted on 2024-06-05, 04:36authored byV Nguyen, D Phung, T Le, H Bui
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.
History
Pagination
2550-2556
Location
Melbourne, Victoria
Start date
2017-08-19
End date
2017-08-25
ISSN
1045-0823
ISBN-13
9780999241103
Language
eng
Publication classification
E Conference publication, E1 Full written paper - refereed
Copyright notice
[2017, The Conference]
Title of proceedings
IJCAI 2017 : Proceedings of the 26th International Joint Conference on Artificial Intelligence