Machine Learning
Modelling high-dimensional data by mixtures of factor analyzers
Computational Statistics & Data Analysis
Penalized factor mixture analysis for variable selection in clustered data
Computational Statistics & Data Analysis
Sparse Bayesian hierarchical modeling of high-dimensional clustering problems
Journal of Multivariate Analysis
Model-based subspace clustering of non-Gaussian data
Neurocomputing
Predicting future reviews: sentiment analysis models for collaborative filtering
Proceedings of the fourth ACM international conference on Web search and data mining
Model-based clustering of high-dimensional data: Variable selection versus facet determination
International Journal of Approximate Reasoning
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Model-based clustering of high-dimensional data: A review
Computational Statistics & Data Analysis
Cluster analysis: unsupervised learning via supervised learning with a non-convex penalty
The Journal of Machine Learning Research
A LASSO-penalized BIC for mixture model selection
Advances in Data Analysis and Classification
Embedded local feature selection within mixture of experts
Information Sciences: an International Journal
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Variable selection in clustering analysis is both challenging and important. In the context of model-based clustering analysis with a common diagonal covariance matrix, which is especially suitable for "high dimension, low sample size" settings, we propose a penalized likelihood approach with an L1 penalty function, automatically realizing variable selection via thresholding and delivering a sparse solution. We derive an EM algorithm to fit our proposed model, and propose a modified BIC as a model selection criterion to choose the number of components and the penalization parameter. A simulation study and an application to gene function prediction with gene expression profiles demonstrate the utility of our method.