Accelerating distributed Expectation–Maximization algorithms with frequent updates

Abstract Expectation–Maximization (EM) is a popular approach for parameter estimation in many applications, such as image understanding, document classification, and genome data analysis. Despite the popularity of EM algorithms, it is challenging to efficiently implement these algorithms in a distributed environment for handling massive data sets. In particular, many EM algorithms that frequently update the parameters have been shown to be much more efficient than their concurrent counterparts. Accordingly, we propose two approaches to parallelize such EM algorithms in a distributed environment so as to scale to massive data sets. We prove that both approaches maintain the convergence properties of the EM algorithms. Based on the approaches, we design and implement a distributed framework, FreEM, to support the implementation of frequent updates for the EM algorithms. We show its efficiency through two categories of EM applications, clustering and topic modeling. These applications include k-means clustering, fuzzy c-means clustering, parameter estimation for the Gaussian Mixture Model, and variational inference for Latent Dirichlet Allocation. We extensively evaluate our framework on both a cluster of local machines and the Amazon EC2 cloud. Our evaluation shows that the EM algorithms with frequent updates implemented on FreEM can converge much faster than those implementations with traditional concurrent updates.