SIAM Journal on Scientific and Statistical Computing
Robust automatic speech recognition with missing and unreliable acoustic data
Speech Communication
Modelling high-dimensional data by mixtures of factor analyzers
Computational Statistics & Data Analysis
Robust Cluster Analysis via Mixtures of Multivariate t-Distributions
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Bayesian analysis of mixture modelling using the multivariate t distribution
Statistics and Computing
Bayesian Analysis of Mixtures of Factor Analyzers
Neural Computation
Forecasting Skewed Biased Stochastic Ozone Days: Analyses and Solutions
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
On fast supervised learning for normal mixture models with missing information
Pattern Recognition
Imputation through finite Gaussian mixture models
Computational Statistics & Data Analysis
Extension of the mixture of factor analyzers model to incorporate the multivariate t-distribution
Computational Statistics & Data Analysis
SMEM Algorithm for Mixture Models
Neural Computation
Forecasting skewed biased stochastic ozone days: analyses, solutions and beyond
Knowledge and Information Systems
Linear mixed models with skew-elliptical distributions: A Bayesian approach
Computational Statistics & Data Analysis
Maximum likelihood estimation for multivariate skew normal mixture models
Journal of Multivariate Analysis
Robust mixture modeling using multivariate skew t distributions
Statistics and Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Maximum likelihood estimation of mixtures of factor analyzers
Computational Statistics & Data Analysis
Modeling the manifolds of images of handwritten digits
IEEE Transactions on Neural Networks
Fast ML Estimation for the Mixture of Factor Analyzers via an ECM Algorithm
IEEE Transactions on Neural Networks
Automated learning of factor analysis with complete and incomplete data
Computational Statistics & Data Analysis
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Mixtures of common factor analyzers (MCFA), thought of as a parsimonious extension of mixture factor analyzers (MFA), have recently been developed as a novel approach to analyzing high-dimensional data, where the number of observations n is not very large relative to their dimension p. The key idea behind MCFA is to reduce further the number of parameters in the specification of the component-covariance matrices. An attractive and important feature of MCFA is to allow visualizing data in lower dimensions. The occurrence of missing data persists in many scientific investigations and often complicates data analysis. In this paper, we establish a computationally flexible EM-type algorithm for parameter estimation of the MCFA model with partially observed data. To facilitate the implementation, two auxiliary permutation matrices are incorporated into the estimating procedure for exactly extracting the location of observed and missing components of each observation. Practical issues related to the specification of initial values, model-based clustering and discriminant procedure are also discussed. Our methodology is illustrated through real and simulated examples.