R package SelvarMix: Regularization for Variable Selection in Model-Based Clustering and Discriminant Analysis. Performs a regularization approach to variable selection in the model-based clustering and classification frameworks. First, the variables are arranged in order with a lasso-like procedure. Second, the method of Maugis, Celeux, and Martin-Magniette (2009, 2011) <doi:10.1016/j.csda.2009.04.013>, <doi:10.1016/j.jmva.2011.05.004> is adapted to define the role of variables in the two frameworks.
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References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Biernacki, Christophe; Lourme, Alexandre: Unifying data units and models in (co-)clustering (2019)
- Fop, Michael; Murphy, Thomas Brendan: Variable selection methods for model-based clustering (2018)
- Luca Scrucca; Adrian Raftery: clustvarsel: A Package Implementing Variable Selection for Gaussian Model-Based Clustering in R (2018) not zbMATH