Large Margin Learning of Gaussian Mixture Models

In our ECML 2010 paper we present a discriminative learning framework for Gaussian mixture models (GMMs) used for classification based on the extended Baum-Welch (EBW) algorithm. We suggest two criteria for discriminative optimization, namely the class conditional likelihood (CL) and the maximization of the margin (MM). In the experiments, we present results for synthetic data, broad phonetic classification, and a remote sensing application. The experiments show that CL-optimized GMMs (CL-GMMs) achieve a lower performance compared to MM-optimized GMMs (MM-GMMs), whereas both discriminative GMMs (DGMMs) perform significantly better than generatively learned GMMs. We also show that the generative discriminatively parameterized GMM classifiers still allow to marginalize over missing features, a case where generative classifiers have an advantage over purely discriminative classifiers such as support vector machines or neural networks.

F. Pernkopf, M. Wohlmayr, "Large Margin Learning of Bayesian Classifiers based on Gaussian Mixture Models", European Conference on Machine Learning (ECML), pp. 50-66, 2010.

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The MM-GMMs and CL-GMMs code can be downloaded here. The full paper is provided in the Publications section or here.

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The database may be used free of charge for research purposes. For other uses and further support please contact Franz Pernkopf.

 

Synthetic spiral data: (a) generative GMM, (b) CL-GMM, (c) MM-GMM, and (d) decision boundary of all learning approaches.
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MM-GMMs