The competitive em algorithm for gaussian mixtures with BYY harmony criterion

Abstract

Gaussian mixture has been widely used for data modeling and analysis and the EM algorithm is generally employed for its parameter learning. However, the EM algorithm may be trapped into a local maximum of the likelihood and even leads to a wrong result if the number of components is not appropriately set. Recently, the competitive EM (CEM) algorithm for Gaussian mixtures, a new kind of split-and-merge learning algorithm with certain competitive mechanism on estimated components of the EM algorithm, has been constructed to overcome these drawbacks. In this paper, we construct a new CEM algorithm through the Bayesian Ying-Yang (BYY) harmony stop criterion, instead of the previously used MML criterion. It is demonstrated by the simulation experiments that our proposed CEM algorithm outperforms the original one on both model selection and parameter estimation. © 2008 Springer-Verlag Berlin Heidelberg.