Statistical analysis with missing data
Statistical analysis with missing data
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
On fast supervised learning for normal mixture models with missing information
Pattern Recognition
Extension of the mixture of factor analyzers model to incorporate the multivariate t-distribution
Computational Statistics & Data Analysis
Choosing an appropriate number of factors in factor analysis with incomplete data
Computational Statistics & Data Analysis
ML estimation for factor analysis: EM or non-EM?
Statistics and Computing
Computationally efficient learning of multivariate t mixture models with missing information
Computational Statistics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast ML Estimation for the Mixture of Factor Analyzers via an ECM Algorithm
IEEE Transactions on Neural Networks
An efficient ECM algorithm for maximum likelihood estimation in mixtures of t-factor analyzers
Computational Statistics
Mixtures of common factor analyzers for high-dimensional data with missing information
Journal of Multivariate Analysis
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In the application of the popular maximum likelihood method to factor analysis, the number of factors is commonly determined through a two-stage procedure, in which stage 1 performs parameter estimation for a set of candidate models and then stage 2 chooses the best according to certain model selection criterion. Usually, to obtain satisfactory performance, a large set of candidates is used and this procedure suffers a heavy computational burden. To overcome this problem, a novel one-stage algorithm is proposed in which parameter estimation and model selection are integrated in a single algorithm. This is obtained by maximizing the criterion with respect to model parameters and the number of factors jointly, rather than separately. The proposed algorithm is then extended to accommodate incomplete data. Experiments on a number of complete/incomplete synthetic and real data reveal that the proposed algorithm is as effective as the existing two-stage procedure while being much more computationally efficient, particularly for incomplete data.