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Model-based mixture discriminant analysis an experimental study Zohar Halbe and Mayer Aladjem
 

Summary: 1
Model-based mixture discriminant analysis an experimental study
Zohar Halbe and Mayer Aladjem
Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev
P.O.Box 653, Beer-Sheva, 84105, Israel
Abstract
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.
Keywords: Discriminant analysis, Gaussian mixture model, Density estimation, Model selection.
1. Introduction
Discriminant analysis (DA) is a powerful technique for classifying observations into known pre-
existing classes. In the Bayesian decision framework [1] a common assumption is that the observed d-
dimensional patterns x (xRd
) are characterized by the class conditional density fc(x), for each class
c=1, 2,..., C. Let Pc denotes a prior probability of the class c. According to Bayes theorem the posterior
probability that an arbitrary observation x belongs to class c is
1
( | ) ( )/ ( ).
C

  

Source: Aladjem, Mayer - Department of Electrical and Computer Engineering, Ben-Gurion University

 

Collections: Computer Technologies and Information Sciences