Model-based mixture discriminant analysis - An experimental study

Zohar Halbe, Mayer Aladjem

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


The subject of this paper is an experimental study of a discriminant analysis (DA) based on Gaussian mixture estimation of the class-conditional densities. Five parameterizations of the covariance matrixes of the Gaussian components are studied. Recommendation for selection of the suitable parameterization of the covariance matrixes is given.

Original languageEnglish
Pages (from-to)437-440
Number of pages4
JournalPattern Recognition
Issue number3
StatePublished - 1 Mar 2005


  • Density estimation
  • Discriminant analysis
  • Gaussian mixture model
  • Model selection

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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