-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).
+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).