SelvarMix (version 1.2.1)
Regularization for Variable Selection in Model-Based Clustering
and Discriminant Analysis
Description
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)
,
is adapted to define the role of variables in the two frameworks.