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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.

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Title Streaming variational inference for dirichlet process mixtures
Author(s) Huynh, Viet
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Venkatesh, SvethaORCID iD for 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

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Created: Mon, 07 Mar 2016, 11:42:47 EST

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