Abstract
This paper presents a new finite mixture model based on a generalization of the Dirichlet distribution. For the estimation of the parameters of this mixture we use a GEM (Generalized Expectation Maximization) algorithm Based on a Newton-Raphson step. The experimental results involve the comparison of the performance of Gaussian and generalized Dirichlet mixtures in the classification of several pattern-recognition data sets.