SelvarMix: A R package for variable selection in model-based clustering and discriminant analysis with a regularization approach
Mohammed Sedki, Gilles Celeux, Cathy Maugis · 2014
In the R package SelvarMix, a regularization approach of variable selection is considered 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 et al. (2009b, 2011) is adapted to define the role of variables in the two frameworks. This variable ranking allows us to avoid the painfully slow stepwise algorithms of Maugis et al. (2009b). Thus, SelvarMix provides a much faster variable selection procedure than Maugis et al. (2009b, 2011) and allows us to study high-dimensional datasets.