Boosting Gaussian mixture models via discriminant analysis

  title={Boosting Gaussian mixture models via discriminant analysis},
  author={Hao Tang and Thomas S. Huang},
  journal={2008 19th International Conference on Pattern Recognition},
The Gaussian mixture model (GMM) can approximate arbitrary probability distributions, which makes it a powerful tool for feature representation and classification. However, it suffers from insufficient training data, especially when the feature space is of high dimensionality. In this paper, we present a novel approach to boost the GMMs via discriminant analysis in which the required amount of training data depends only upon the number of classes, regardless of the feature dimension. We… CONTINUE READING


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