Variational learning of finite Dirichlet mixture models using component splitting

Finite Dirichlet mixture models have proved to be an effective knowledge representation and inference engine in several machine learning and data mining applications. In this paper, we address the task of learning and selecting finite Dirichlet mixture models in an incremental variational way. A learning algorithm based on component splitting and local model selection is proposed. The merits of the proposed approach are illustrated using synthetic data as well as real challenging applications involving object detection, text documents clustering and distinguishing photographic images from computer graphics.