A Dirichlet process mixture of dirichlet distributions for classification and prediction

A significant problem in clustering is the determination of the number of classes which best describes the data. This paper proposes a learning approach based on both Dirichlet process and Dirichlet distribution which provide flexible nonparametric Bayesian framework for non-Gaussian data clustering. Our approach is Bayesian and relies on the estimation of the posterior distribution of clusterings using Gibbs sampler. The experimental results involve data classification and image models prediction, and show the merits of our approach.