Simultaneous Feature Selection and Clustering Using Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-based cluster and discriminant analysis with the MIXMOD software
Computational Statistics & Data Analysis
Editorial: Special issue on variable selection and robust procedures
Computational Statistics & Data Analysis
Variable selection in model-based discriminant analysis
Journal of Multivariate Analysis
Multivariate linear regression with non-normal errors: a solution based on mixture models
Statistics and Computing
Clustering student skill set profiles in a unit hypercube using mixtures of multivariate betas
Advances in Data Analysis and Classification
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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The currently available variable selection procedures in model-based clustering assume that the irrelevant clustering variables are all independent or are all linked with the relevant clustering variables. A more versatile variable selection model is proposed, taking into account three possible roles for each variable: The relevant clustering variables, the irrelevant clustering variables dependent on a part of the relevant clustering variables and the irrelevant clustering variables totally independent of all the relevant variables. A model selection criterion and a variable selection algorithm are derived for this new variable role modeling. The model identifiability and the consistency of the variable selection criterion are also established. Numerical experiments highlight the interest of this new modeling.