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Streaming variational inference for dirichlet process mixtures
conference contribution
posted on 2016-01-01, 00:00 authored by Huu Viet Huynh, Quoc-Dinh Phung, Svetha VenkateshSvetha VenkateshBayesian 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.
History
Event
Asian Conference on Machine Learning (7th : 2015 : Hong Kong)Volume
45Series
JMLR Workshop and Conference ProceedingsPagination
237 - 252Publisher
JMLRLocation
Hong Kong, PRCPlace of publication
Cambridge, Ma.Start date
2015-11-20End date
2015-11-22Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2016, JMLREditor/Contributor(s)
G Holmes, T LiuTitle of proceedings
ACML 2015: Proceedings of the 7th Asian Conference on Machine LearningUsage metrics
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