Entropy penalized learning for Gaussian mixture models

  title={Entropy penalized learning for Gaussian mixture models},
  author={Boyu Wang and Feng Wan and Peng Un Mak and Pui-in Mak and Mang I Vai},
  journal={The 2011 International Joint Conference on Neural Networks},
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… CONTINUE READING