MAP for Exponential Family Dirichlet Process Mixture Models

The Dirichlet process mixture (DPM) is a ubiquitous, flexible Bayesian nonparametric model. However, full probabilistic inference in this model is analytically intractable, so that computationally intensive techniques such as Gibb’s sampling are required. As a result, DPM-based methods, which have considerable potential, are restricted to applications in which computational resources and time for inference is plentiful. For example, they would not be practical for digital signal processing on embedded hardware, where computational resources are at a serious premium. Here we develop simplified yet statistically rigorous approximate maximum a-posteriori (MAP) inference algorithms for DPMs, which we call MAPDP. This algorithm is as simple as K-means clustering, performs in experiments as well as Gibb’s sampling, while requiring only a fraction of the computational effort. Unlike related small variance asymptotics, our algorithm is non-degenerate and so inherits the “rich get richer” property of the Dirichlet process. It also retains a non-degenerate, closed-form likelihood which enables standard tools such as cross-validation to be used. This is a well-posed approximation to the MAP solution of the probabilistic DPM model.