Competitive EM algorithm for finite mixture models

In this paper, we present a novel competitive EM (CEM) algorithm for finite mixture models to overcome the two main drawbacks of the EM algorithm: often getting trapped at local maxima and sometimes converging to the boundary of the parameter space. The proposed algorithm is capable of automatically choosing the clustering number and selecting the "split" or "merge" operations efficiently based on the new competitive mechanism we propose. It is insensitive to the initial configuration of the mixture component number and model parameters. Experiments on synthetic data show that our algorithm has very promising performance for the parameter estimation of mixture models. The algorithm is also applied to the structure analysis of complicated Chinese characters. The results show that the proposed algorithm performs much better than previous methods with slightly heavier computation burden. (C) 2003 Published by Elsevier Ltd on behalf of Pattern Recognition Society.