Mixtures of common factor analyzers for high-dimensional data with missing information
Journal of Multivariate Analysis
Model-based clustering of high-dimensional data streams with online mixture of probabilistic PCA
Advances in Data Analysis and Classification
Automated learning of factor analysis with complete and incomplete data
Computational Statistics & Data Analysis
Mixtures of regressions with changepoints
Statistics and Computing
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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.