Online variational learning of finite Dirichlet mixture models

In this paper, we present an online variational inference algorithm for finite Dirichlet mixture models learning. Online algorithms allow data points to be processed one at a time, which is important for real-time applications, and also where large scale data sets are involved so that batch processing of all data points at once becomes infeasible. By adopting the variational Bayes framework in an online manner, all the involved parameters and the model complexity (i.e. the number of components) of the Dirichlet mixture model can be estimated simultaneously in a closed form. The proposed algorithm is validated through both synthetic data sets and a challenging real-world application namely video background subtraction.