Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 425730, 11 pages
Research Article

A Batch Rival Penalized Expectation-Maximization Algorithm for Gaussian Mixture Clustering with Automatic Model Selection

1Faculty of Applied Mathematics, Guangdong University of Technology, Guangzhou 510520, China
2Department of Computer Science, Hong Kong Baptist University, Kowloon, Hong Kong
3Department of Electronics and Information Engineering, Huazhong University of Science &Technology, Wuhan, China

Received 31 August 2011; Accepted 9 October 2011

Academic Editor: Sheng-yong Chen

Copyright © 2012 Jiechang Wen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Within the learning framework of maximum weighted likelihood (MWL) proposed by Cheung, 2004 and 2005, this paper will develop a batch Rival Penalized Expectation-Maximization (RPEM) algorithm for density mixture clustering provided that all observations are available before the learning process. Compared to the adaptive RPEM algorithm in Cheung, 2004 and 2005, this batch RPEM need not assign the learning rate analogous to the Expectation-Maximization (EM) algorithm (Dempster et al., 1977), but still preserves the capability of automatic model selection. Further, the convergence speed of this batch RPEM is faster than the EM and the adaptive RPEM in general. The experiments show the superior performance of the proposed algorithm on the synthetic data and color image segmentation.