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The Gaussian mixture model is a classic technique for clustering and datamodeling that is used in numerous applications. With the rise of big data,there is a need for parameter estimation techniques that can handle streamingdata and distribute the computation over several processors. While onlinevariants of the Expectation Maximization (EM) algorithm exist, their dataefficiency is reduced by a stochastic approximation of the E-step and it is notclear how to distribute the computation over multiple processors. We propose aBayesian learning technique that lends itself naturally to online anddistributed computation. Since the Bayesian posterior is not tractable, weproject it onto a family of tractable distributions after each observation bymatching a set of sufficient moments. This Bayesian moment matching techniquecompares favorably to online EM in terms of time and accuracy on a set of datamodeling benchmarks.

Online and Distributed learning of Gaussian mixture models by Bayesian Moment Matching