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. Attached Files Name Description MIMEType Size Downloads Title Discriminative Bayesian nonparametric clustering Author(s) Nguyen, Vu Phung, Dinh orcid.org/0000-0002-9977-8247 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 Collection: Centre for Pattern Recognition and Data Analytics Related Links Link Description   Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Fri, 03 Nov 2017, 20:56:59 EST Sat, 04 Nov 2017, 04:01:09 EST Wed, 22 Nov 2017, 13:16:29 EST Thu, 23 Nov 2017, 05:02:44 EST Tue, 28 Nov 2017, 14:12:42 EST Tue, 28 Nov 2017, 14:16:43 EST Tue, 08 May 2018, 10:41:21 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 77 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Wed, 22 Nov 2017, 13:16:29 EST

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