Variable selection for supervised classification in high
dimension
Description
The functions provided in the FADA (Factor Adjusted Discriminant Analysis) package aim at performing
supervised classification models and variable selection on dependent
covariates. The classification procedures are combined with a factor
modeling of dependence among covariates. The available procedures are Lasso
regularized logistic model (see Friedman et al. (2010)), sparse linear
discriminant analysis (see Clemmensen et al. (2011)), shrinkage linear and
diagonal discriminant analysis (see M. Ahdesmaki et al. (2010)).