A batch rival penalized expectation-maximization algorithm for gaussian mixture clustering with automatic model selection

Jiechang Wen, Dan Zhang, Yiu Ming CHEUNG*, Hailin Liu, Xinge You

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number425730
JournalComputational and Mathematical Methods in Medicine
Volume2012
DOIs
Publication statusPublished - 2012

Scopus Subject Areas

  • Modelling and Simulation
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Applied Mathematics

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