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Regularized Mixture Discriminant Analysis Zohar Halbe and Mayer Aladjem
 

Summary: 1
Regularized Mixture Discriminant Analysis
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 In this paper we seek a Gaussian mixture model (GMM) of the class-
conditional densities for plug-in Bayes classification. We propose a method for setting
the number of the components and the covariance matrices of the class-conditional
GMMs. It compromises between simplicity of the model selection based on the
Bayesian information criterion (BIC) and the high accuracy of the model selection based
on the cross-validation (CV) estimate of the correct classification rate. We apply an idea
of Friedman (1989) to shrink a predefined covariance matrix to a parameterization with
substantially reduced degrees of freedom (reduced number of the adjustable
parameters). Our method differs from the original Friedman's method by the meaning of
the shrinkage. We operate on matrices computed for a certain class while the Friedman's
method shrinks matrices from different classes. We compare our method with the
conventional methods for setting the GMMs based on the BIC and CV. The
experimental results show that our method has the potential to produce
parameterizations of the covariance matrices of the GMMs which are better than the
parameterizations used in other methods. We observed significant enlargement of the

  

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

 

Collections: Computer Technologies and Information Sciences