Scalable Estimation of Dirichlet Process Mixture Models on Distributed Data
Refereed conference paper presented and published in conference proceedings

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AbstractWe consider the estimation of Dirichlet Process Mixture Models (DPMMs) in distributed environments, where data are distributed across multiple computing nodes. A key advantage of Bayesian nonparametric models such as DPMMs is that they allow new components to be introduced on the fly as needed. This, however, posts an important challenge to distributed estimation -- how to handle new components efficiently and consistently. To tackle this problem, we propose a new estimation method, which allows new components to be created locally in individual computing nodes. Components corresponding to the same cluster will be identified and merged via a probabilistic consolidation scheme. In this way, we can maintain the consistency of estimation with very low communication cost. Experiments on large real-world data sets show that the proposed method can achieve high scalability in distributed and asynchronous environments without compromising the mixing performance.
All Author(s) ListRuohui Wang, Dahua Lin
Name of ConferenceInternational Joint Conference on Artificial Intelligence (IJCAI)
Start Date of Conference19/08/2017
End Date of Conference25/08/2017
Place of ConferenceMelbourne
Country/Region of ConferenceAustralia
Proceedings TitleProceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Year2017
Month8
Pages4632 - 4639
LanguagesEnglish-United States

Last updated on 2021-19-02 at 00:11