EM Algorithms for Probabilistic Mapping Networks
Identifieur interne : 00D104 ( Main/Merge ); précédent : 00D103; suivant : 00D105EM Algorithms for Probabilistic Mapping Networks
Auteurs : Haizhou Li [France] ; Yifan Gong [France] ; Jean-Paul Haton [France]Source :
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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
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type="university">Université Nancy 2</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Institut national polytechnique de Lorraine</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName></affiliation></author><author><name sortKey="Haton, Jean Paul" sort="Haton, Jean Paul" uniqKey="Haton J" first="Jean-Paul" last="Haton">Jean-Paul Haton</name><affiliation wicri:level="1"><hal:affiliation type="researchteam" xml:id="struct-2365" status="OLD"><idno type="RNSR">198622106U</idno><orgName>Basic Models and Applications of Perceptive and Cognitive Processes</orgName><orgName type="acronym">SYCO</orgName><desc><address><country key="FR"></country></address><ref 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type="acronym">Inria</orgName><desc><address><addrLine>Domaine de VoluceauRocquencourt - BP 10578153 Le Chesnay Cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.inria.fr/en/</ref></desc></org></tutelle><tutelle active="#struct-300291" type="indirect"><org type="institution" xml:id="struct-300291" status="OLD"><orgName>Université Henri Poincaré - Nancy 1</orgName><orgName type="acronym">UHP</orgName><date type="end">2011-12-31</date><desc><address><addrLine>24-30 rue Lionnois, BP 60120, 54 003 NANCY cedex, France</addrLine><country key="FR"></country></address></desc></org></tutelle><tutelle active="#struct-300292" type="indirect"><org type="institution" xml:id="struct-300292" status="OLD"><orgName>Université Nancy 2</orgName><date type="end">2011-12-31</date><desc><address><addrLine>91 avenue de la Libération, BP 454, 54001 Nancy cedex</addrLine><country key="FR"></country></address></desc></org></tutelle><tutelle active="#struct-300293" type="indirect"><org type="institution" xml:id="struct-300293" status="OLD"><orgName>Institut National Polytechnique de Lorraine</orgName><orgName type="acronym">INPL</orgName><date type="end">2011-12-31</date><desc><address><country key="FR"></country></address></desc></org></tutelle><tutelle active="#struct-2496" type="direct"><org type="laboratory" xml:id="struct-2496" status="OLD"><orgName>INRIA Lorraine</orgName><desc><address><addrLine>615 rue du Jardin Botanique 54600 Villers-lès-Nancy</addrLine><country key="FR"></country></address><ref type="url">http://www.inria.fr/centre-de-recherche-inria/nancy-grand-est</ref></desc><listRelation><relation active="#struct-300009" type="direct"></relation></listRelation></org></tutelle></tutelles></hal:affiliation><country>France</country><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Université Nancy 2</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName><placeName><settlement type="city">Nancy</settlement><region type="region" nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Institut national polytechnique de Lorraine</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>Em algorithm</term><term>maximum likelihood classifier</term><term>probabilistic mapping network</term><term>speaker recognition</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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