- 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)
+ 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).