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

TitleFast ML estimation for the mixture of factor analyzers via an ECM algorithm
Authors
KeywordsAlternating expectation conditional maximization (AECM)
Expectation conditional maximization (ECM)
Expectation maximization (EM)
Maximum-likelihood estimation (MLE)
Mixture of factor analyzers (MFA)
Issue Date2008
PublisherIEEE.
Citation
Ieee Transactions On Neural Networks, 2008, v. 19 n. 11, p. 1956-1961 How to Cite?
AbstractIn this brief, we propose a fast expectation conditional maximization (ECM) algorithm for maximum-likelihood (ML) estimation of mixtures of factor analyzers (MFA). Unlike the existing expectation-maximization (EM) algorithms such as the EM in Ghahramani and Hinton, 1996, and the alternating ECM (AECM) in McLachlan and Peel, 2003, where the missing data contains component-indicator vectors as well as latent factors, the missing data in our ECM consists of component-indicator vectors only. The novelty of our algorithm is that closed-form expressions in all conditional maximization (CM) steps are obtained explicitly, instead of resorting to numerical optimization methods. As revealed by experiments, the convergence of our ECM is substantially faster than EM and AECM regardless of whether assessed by central processing unit (CPU) time or number of iterations. © 2008 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/125405
ISSN
2011 Impact Factor: 2.952
ISI Accession Number ID
Funding AgencyGrant Number
Council of the Hong Kong Special Administrative Region, ChinaHKU 7176/02H
Funding Information:

This work was supported by the Research Grants Council of the Hong Kong Special Administrative Region, China, under Project HKU 7176/02H.

References
Grants

 

DC FieldValueLanguage
dc.contributor.authorZhao, JHen_HK
dc.contributor.authorYu, PLHen_HK
dc.date.accessioned2010-10-31T11:29:32Z-
dc.date.available2010-10-31T11:29:32Z-
dc.date.issued2008en_HK
dc.identifier.citationIeee Transactions On Neural Networks, 2008, v. 19 n. 11, p. 1956-1961en_HK
dc.identifier.issn1045-9227en_HK
dc.identifier.urihttp://hdl.handle.net/10722/125405-
dc.description.abstractIn this brief, we propose a fast expectation conditional maximization (ECM) algorithm for maximum-likelihood (ML) estimation of mixtures of factor analyzers (MFA). Unlike the existing expectation-maximization (EM) algorithms such as the EM in Ghahramani and Hinton, 1996, and the alternating ECM (AECM) in McLachlan and Peel, 2003, where the missing data contains component-indicator vectors as well as latent factors, the missing data in our ECM consists of component-indicator vectors only. The novelty of our algorithm is that closed-form expressions in all conditional maximization (CM) steps are obtained explicitly, instead of resorting to numerical optimization methods. As revealed by experiments, the convergence of our ECM is substantially faster than EM and AECM regardless of whether assessed by central processing unit (CPU) time or number of iterations. © 2008 IEEE.en_HK
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofIEEE Transactions on Neural Networksen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsIEEE Transactions on Neural Networks. Copyright © IEEE.-
dc.rights©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.subjectAlternating expectation conditional maximization (AECM)en_HK
dc.subjectExpectation conditional maximization (ECM)en_HK
dc.subjectExpectation maximization (EM)en_HK
dc.subjectMaximum-likelihood estimation (MLE)en_HK
dc.subjectMixture of factor analyzers (MFA)en_HK
dc.subject.meshAlgorithms-
dc.subject.meshArtificial Intelligence-
dc.subject.meshFactor Analysis, Statistical-
dc.subject.meshLikelihood Functions-
dc.subject.meshModels, Statistical-
dc.titleFast ML estimation for the mixture of factor analyzers via an ECM algorithmen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1045-9227&volume=19&issue=11&spage=1956&epage=1961&date=2008&atitle=Fast+ML+estimation+for+the+mixture+of+factor+analyzers+via+an+ECM+algorithm-
dc.identifier.emailYu, PLH: plhyu@hkucc.hku.hken_HK
dc.identifier.authorityYu, PLH=rp00835en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/TNN.2008.2003467en_HK
dc.identifier.pmid19000964-
dc.identifier.scopuseid_2-s2.0-56449104991en_HK
dc.identifier.hkuros180282en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-56449104991&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume19en_HK
dc.identifier.issue11en_HK
dc.identifier.spage1956en_HK
dc.identifier.epage1961en_HK
dc.identifier.isiWOS:000260865800009-
dc.publisher.placeUnited Statesen_HK
dc.relation.projectSpatial models for multiple ranking data and their applications-
dc.identifier.scopusauthoridZhao, JH=7410313775en_HK
dc.identifier.scopusauthoridYu, PLH=7403599794en_HK

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