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An Entropy Maximization Approach to Optimal Model Selection in Gaussian Mixtures

  • Antonio Peñalver
  • Juan M. Sáez
  • Francisco Escolano
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2905)

Abstract

In this paper we address the problem of estimating the parameters of a Gaussian mixture model. Although the EM (Expectation-Maximization) algorithm yields the maximum-likelihood solution it has many problems: (i) it requires a careful initialization of the parameters; (ii) the optimal number of kernels in the mixture may be unknown before-hand. We propose a criterion based on the entropy of the pdf (probability density function) associated to each kernel to measure the quality of a given mixture model, and a modification of the classical EM algorithm to find the optimal number of kernels in the mixture. We test this method with synthetic and real data and compare the results with those obtained with the classical EM with a fixed number of kernels.

Keywords

Maximum Entropy Gaussian Mixture Model Entropy Maximization Approach Fuse Kernel Neural Processing Letter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Antonio Peñalver
    • 1
  • Juan M. Sáez
    • 1
  • Francisco Escolano
    • 1
  1. 1.Robot Vision Group Departamento de Ciencia de la Computación e Inteligencia ArtificialUniversidad de AlicanteSpain

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