Entropy penalized learning for Gaussian mixture models

In this paper, we propose an entropy penalized approach to address the problem of learning the parameters of Gaussian mixture models (GMMs) with components of small weights. In addition, since the method is based on minimum message length (MML) criterion, it can also determine the number of components of the mixture model. The simulation results demonstrate that our method outperform several other state-of-art model selection algorithms especially for the mixtures with components of very different weights.