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
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
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