On weight design of maximum weighted likelihood and an extended EM algorithm

Zhenyue Zhang*, Yiu Ming CHEUNG

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

10 Citations (Scopus)


The recent Maximum Weighted Likelihood (MWL) [18], [19] has provided a general learning paradigm for density-mixture model selection and learning, in which weight design, however, is a key issue. This paper will therefore explore such a design, and through which a heuristic extended Expectation-Maximization (X-EM) algorithm is presented accordingly. Unlike the EM algorithm [1], the X-EM algorithm is able to perform model selection by fading the redundant components out from a density mixture, meanwhile estimating the model parameters appropriately. The numerical simulations demonstrate the efficacy of our algorithm.

Original languageEnglish
Article number1683776
Pages (from-to)1429-1434
Number of pages6
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number10
Publication statusPublished - Oct 2006

Scopus Subject Areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

User-Defined Keywords

  • Extended expectation-maximization algorithm
  • Maximum weighted likelihood
  • Model selection
  • Weight design


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