MML-based approach for finite dirichlet mixture estimation and selection

Abstract

This paper proposes an unsupervised algorithm for learning a finite Dirichlet mixture model. An important part of the unsupervised learning problem is determining the number of clusters which best describe the data. We consider here the application of the Minimum Message length (MML) principle to determine the number of clusters. The Model is compared with results obtained by other selection criteria (AIC, MDL, MMDL, PC and a Bayesian method). The proposed method is validated by synthetic data and summarization of texture image database. © Springer-Verlag Berlin Heidelberg 2005.