-Description: 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, published in Advances in Data Analysis and Clustering (2016)
-Author: Benjamin Auder <Benjamin.Auder@math.u-psud.fr> [aut,cre],
+Description: 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, published in Advances in Data Analysis and Clustering (2016).
+Author: Benjamin Auder <benjamin.auder@universite-paris-saclay.fr> [aut,cre],
+ Emilie Devijver <Emilie.Devijver@kuleuven.be> [aut],