Discriminative training of subspace Gaussian mixture model for pattern classification

Authors:
Xiao-Hua Liu;Cheng-Lin Liu
Affiliations:
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P.R. China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, P.R. China
Venue:
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Year:
2010

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Abstract

The Gaussian mixture model (GMM) has been widely used in pattern recognition problems for clustering and probability density estimation. For pattern classification, however, the GMM has to consider two issues: model structure in high-dimensional space and discriminative training for optimizing the decision boundary. In this paper, we propose a classification method using subspace GMM density model and discriminative training. During discriminative training under the minimum classification error (MCE) criterion, both the GMM parameters and the subspace parameters are optimized discriminatively. Our experimental results on the MNIST handwritten digit data and UCI datasets demonstrate the superior classification performance of the proposed method.