A batch rival penalized em algorithm for gaussian mixture clustering with automatic model selection

Dan Zhang*, Yiu Ming CHEUNG

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

1 Citation (Scopus)

Abstract

Cheung [2] has recently proposed a general learning framework, namely Maximum Weighted Likelihood (MWL), in which an adaptive Rival Penalized EM (RPEM) algorithm has been successfully developed for density mixture clustering with automatic model selection. Nevertheless, its convergence speed relies on the value of learning rate. In general, selection of an appropriate learning rate is a nontrivial task. To circumvent such a selection, this paper further studies the MWL learning framework, and develops a batch RPEM algorithm accordingly provided that all observations are available before the learning process. Compared to the adaptive RPEM algorithm, this batch RPEM need not assign the learning rate analogous to the EM, but still preserve the capability of automatic model selection. Further, the convergence speed of this batch RPEM is faster than the EM and the adaptive RPEM. The experiments show the efficacy of the proposed algorithm.

Original languageEnglish
Title of host publicationRough Sets and Knowledge Technology - Second International Conference, RSKT 2007, Proceedings
PublisherSpringer Verlag
Pages252-259
Number of pages8
ISBN (Print)9783540724575
DOIs
Publication statusPublished - 2007
Event2nd International Conference on Rough Sets and Knowledge Technology, RSKT 2007 - Toronto, Canada
Duration: 14 May 200716 May 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4481 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Conference on Rough Sets and Knowledge Technology, RSKT 2007
Country/TerritoryCanada
CityToronto
Period14/05/0716/05/07

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • Learning rate
  • Maximum weighted likelihood
  • Rival penalized expectation-maximization algorithm

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