Estimation of Single-Gaussian and Gaussian Mixture Models for Pattern Recognition

  • Jan Vaněk
  • Lukáš Machlica
  • Josef Psutka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8258)

Abstract

Single-Gaussian and Gaussian-Mixture Models are utilized in various pattern recognition tasks. The model parameters are estimated usually via Maximum Likelihood Estimation (MLE) with respect to available training data. However, if only small amount of training data is available, the resulting model will not generalize well. Loosely speaking, classification performance given an unseen test set may be poor. In this paper, we propose a novel estimation technique of the model variances. Once the variances were estimated using MLE, they are multiplied by a scaling factor, which reflects the amount of uncertainty present in the limited sample set. The optimal value of the scaling factor is based on the Kullback-Leibler criterion and on the assumption that the training and test sets are sampled from the same source distribution. In addition, in the case of GMM, the proper number of components can be determined.

Keywords

Maximum Likelihood Estimation Gaussian Mixture Model Kullback-Leibler Divergence Variance Scaling 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jan Vaněk
    • 1
  • Lukáš Machlica
    • 1
  • Josef Psutka
    • 1
  1. 1.Faculty of Applied Sciences, Department of CyberneticsUniversity of West Bohemia in PilsenPilsenCzech Republic

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