Classifying with Gaussian Mixtures and Clusters

Part of Advances in Neural Information Processing Systems 7 (NIPS 1994)

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Nanda Kambhatla, Todd Leen


In this paper, we derive classifiers which are winner-take-all (WTA) approximations to a Bayes classifier with Gaussian mixtures for class conditional densities. The derived classifiers include clustering based algorithms like LVQ and k-Means. We propose a constrained rank Gaussian mixtures model and derive a WTA algorithm for it. Our experiments with two speech classification tasks indicate that the constrained rank model and the WTA approximations improve the performance over the unconstrained models.