A powerful finite mixture model based on the generalized Dirichlet distribution: unsupervised learning and applications
Nizar Bouguila, D. Ziou · Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. · 2004
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.