Fast ML estimation for the mixture of factor analyzers via an ECM algorithm

IEEE Transactions on Neural Networks

Volumn 19, Issue 11, 2008, Pages 1956-1961

  Zhao, Jian Hua ^a,b   Yu, Philip L H ^a  

^a UNIVERSITY OF HONG KONG   (Hong Kong)

^b YUNNAN UNIVERSITY   (China)

Author keywords

Alternating expectation conditional maximization (AECM); Expectation conditional maximization (ECM); Expectation maximization (EM); Maximum likelihood estimation (MLE); Mixture of factor analyzers (MFA)

Indexed keywords

CONVERGENCE OF NUMERICAL METHODS; ELECTRIC DISCHARGE MACHINING; MAXIMUM LIKELIHOOD; MAXIMUM LIKELIHOOD ESTIMATION; MAXIMUM PRINCIPLE; MIXTURES; NUMERICAL METHODS; PRINCIPAL COMPONENT ANALYSIS; SPEECH ANALYSIS; VECTORS;

ALTERNATING EXPECTATION CONDITIONAL MAXIMIZATION (AECM); EXPECTATION CONDITIONAL MAXIMIZATION (ECM); EXPECTATION MAXIMIZATION (EM); MAXIMUM-LIKELIHOOD ESTIMATION (MLE); MIXTURE OF FACTOR ANALYZERS (MFA);

OPTIMIZATION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER SIMULATION; FACTORIAL ANALYSIS; METHODOLOGY; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; FACTOR ANALYSIS, STATISTICAL; LIKELIHOOD FUNCTIONS; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED;

EID: 56449104991     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2008.2003467     Document Type: Article

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