  We 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.
