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

Jian-hua ZHAO, Leung Ho Philip YU

Research output: Contribution to journalArticles

21 Citations (Scopus)

Abstract

In 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. Copyright © 2008 IEEE.
Original languageEnglish
Pages (from-to)1956-1961
JournalIEEE Transactions on Neural Networks
Volume19
Issue number11
Early online dateSep 2008
DOIs
Publication statusPublished - Nov 2008

Citation

Zhao, J.-H., & Yu, P. L. H. (2008). Fast ML estimation for the mixture of factor analyzers via an ECM algorithm. IEEE Transactions on Neural Networks, 19(11), 1956-1961. doi: 10.1109/TNN.2008.2003467

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