EM Algorithms for Probabilistic Mapping Networks

Haizhou Li 1 Yifan Gong 1 Jean-Paul Haton 1
1 SYCO - Basic Models and Applications of Perceptive and Cognitive Processes
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : The Expectation-Maximization (EM) algorithm is a general technique for maximum likelihood estimation (MLE). In this paper we present several of the important theoretical and practical issues associated with Gaussian mixture modeling (GMM) within the EM framework. First, we propose an EM algorithm for estimating the parameters of a special GMM structure, named a probablistic mapping network (PMN), where the Gaussian probability density function is realized as an internal node. In this way, the EM algorithm is extended to deal with the supervised learning of a multicategory classification problem and serve as a parameter estimator of the neural network Gaussian classifier. Then, a generalized EM (GEM) algorithm is developed as an alternative to the MLE problem of PMN. This is followed by a discussion on the computational considerations and algorithmic comparisons. It is shown that GEM converges faster than EM to the same solution space. The computational efficiency and the numerical stability of the training algorithm benefit from the well-established EM framework. The effectiveness of the proposed PMN architecture and developed EM algorithms are assessed by conducting a set of speaker recognition experiments
Type de document :
[Research Report] RR-2614, INRIA. 1995, pp.39
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Soumis le : mercredi 24 mai 2006 - 14:26:12
Dernière modification le : jeudi 11 janvier 2018 - 06:20:00
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  • HAL Id : inria-00074071, version 1



Haizhou Li, Yifan Gong, Jean-Paul Haton. EM Algorithms for Probabilistic Mapping Networks. [Research Report] RR-2614, INRIA. 1995, pp.39. 〈inria-00074071〉



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