Two methods are implemented to cluster data with finite mixture
regression models. Those procedures deal with high-dimensional covariates and
responses through a variable selection procedure based on the Lasso estimator.
A low-rank constraint could be added, computed for the Lasso-Rank procedure.
A collection of models is constructed, varying the level of sparsity and the
number of clusters, and a model is selected using a model selection criterion
(slope heuristic, BIC or AIC). Details of the procedure are provided in
"Model-based clustering for high-dimensional data. Application to functional data"
by Emilie Devijver (2016) ,
published in Advances in Data Analysis and Clustering.