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Discriminative Bayesian nonparametric clustering

Version 2 2024-06-05, 04:36
Version 1 2017-11-03, 21:56
conference contribution
posted on 2024-06-05, 04:36 authored by V 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

Event

Artificial Intelligence. International Joint Conference (26 : 2017 : Melbourne, Vic)

Publisher

AAAI Press

Place of publication

Morgantown, W. Va.

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