A Generalized Competitive Learning Algorithm on Gaussian Mixture with Automatic Model Selection

摘要

Derived from regularization theory, an adaptive entropy regularized likelihood (ERL) learning algorithm is presented for Gaussian mixture modeling, which is then proved to be actually a generalized competitive learning. The simulation experiments demonstrate that our adaptive ERL learning algorithm can make the parameter estimation with automatic model selection for Gaussian mixture even when two or more Gaussians are overlapped in a high degree.