Streaming variational inference for dirichlet process mixtures Huynh, Viet, Phung, Dinh and Venkatesh, Svetha 2016, Streaming variational inference for dirichlet process mixtures, in ACML 2015: Proceedings of the 7th Asian Conference on Machine Learning, JMLR, Cambridge, Ma., pp. 237-252. Attached Files Name Description MIMEType Size Downloads Title Streaming variational inference for dirichlet process mixtures Author(s) Huynh, Viet Phung, Dinh orcid.org/0000-0002-9977-8247 Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Conference name Asian Conference on Machine Learning (7th : 2015 : Hong Kong) Conference location Hong Kong, PRC Conference dates 20-22 Nov. 2015 Title of proceedings ACML 2015: Proceedings of the 7th Asian Conference on Machine Learning Editor(s) Holmes, G Liu, TY Publication date 2016 Series JMLR Workshop and Conference Proceedings Start page 237 End page 252 Total pages 16 Publisher JMLR Place of publication Cambridge, Ma. Summary Bayesian nonparametric models are theoretically suitable to learn streaming data due to their complexity relaxation to the volume of observed data. However, most of the existing variational inference algorithms are not applicable to streaming applications since they re-quire truncation on variational distributions. In this paper, we present two truncation-free variational algorithms, one for mix-membership inference called TFVB (truncation-free variational Bayes), and the other for hard clustering inference called TFME (truncation-free maximization expectation). With these algorithms, we further developed a streaming learning framework for the popular Dirichlet process mixture (DPM) models. Our ex-periments demonstrate the usefulness of our framework in both synthetic and real-world data. Language eng Field of Research 080109 Pattern Recognition and Data Mining Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category E1 Full written paper - refereed ERA Research output type E Conference publication Copyright notice ©2016, JMLR Persistent URL http://hdl.handle.net/10536/DRO/DU:30081949 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Centre for Pattern Recognition and Data Analytics (PRADA) 2018 ERA Submission Related Links Link Description Publisher URL Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 07 Mar 2016, 11:42:47 EST Tue, 08 Mar 2016, 04:03:45 EST Tue, 08 Mar 2016, 05:09:13 EST Wed, 04 May 2016, 12:22:59 EST Wed, 04 May 2016, 12:23:49 EST Sat, 12 Aug 2017, 02:31:47 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 1 times in Scopus Search Google Scholar Access Statistics: 492 Abstract Views, 4 File Downloads  -  Detailed Statistics Created: Mon, 07 Mar 2016, 11:42:47 EST

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