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Discriminative Bayesian nonparametric clustering

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.

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Title Discriminative Bayesian nonparametric clustering
Author(s) Nguyen, Vu
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Le, TrungORCID iD for Le, Trung orcid.org/0000-0002-7070-8093
Bui, Hung
Conference name Artificial Intelligence. International Joint Conference (26th : 2017 : Melbourne, Vic)
Conference location Melbourne, Victoria
Conference dates 2017/08/19 - 2017/08/25
Title of proceedings 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.
ISBN 9780999241103
ISSN 1045-0823
Language eng
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©[2017, The Conference]
Persistent URL http://hdl.handle.net/10536/DRO/DU:30104237

Document type: Conference Paper
Collections: Centre for Pattern Recognition and Data Analytics
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