Mixtures of probabilistic principal component analyzers
Neural Computation
Modelling high-dimensional data by mixtures of factor analyzers
Computational Statistics & Data Analysis
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Enhanced Model-Based Clustering, Density Estimation,and Discriminant Analysis Software: MCLUST
Journal of Classification
Penalized Model-Based Clustering with Application to Variable Selection
The Journal of Machine Learning Research
Parsimonious Gaussian mixture models
Statistics and Computing
Bayesian variable selection and model averaging in the arbitrage pricing theory model
Computational Statistics & Data Analysis
Using conditional independence for parsimonious model-based Gaussian clustering
Statistics and Computing
Model-based clustering of high-dimensional data: A review
Computational Statistics & Data Analysis
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A model-based clustering approach which contextually performs dimension reduction and variable selection is presented. Dimension reduction is achieved by assuming that the data have been generated by a linear factor model with latent variables modeled as Gaussian mixtures. Variable selection is performed by shrinking the factor loadings though a penalized likelihood method with an L1 penalty. A maximum likelihood estimation procedure via the EM algorithm is developed and a modified BIC criterion to select the penalization parameter is illustrated. The effectiveness of the proposed model is explored in a Monte Carlo simulation study and in a real example.