R package FADA: Variable Selection for Supervised Classification in High Dimension. The functions provided in the FADA (Factor Adjusted Discriminant Analysis) package aim at performing supervised classification of high-dimensional and correlated profiles. The procedure combines a decorrelation step based on a factor modeling of the dependence among covariates and a classification method. The available methods 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)). More methods of classification can be used on the decorrelated data provided by the package FADA.
Keywords for this software
References in zbMATH (referenced in 3 articles , 1 standard article )
Showing results 1 to 3 of 3.
- Devijver, Emilie; Perthame, Emeline: Prediction regions through inverse regression (2020)
- Yu, Weichang; Ormerod, John T.; Stewart, Michael: Variational discriminant analysis with variable selection (2020)
- Perthame, Émeline; Friguet, Chloé; Causeur, David: Stability of feature selection in classification issues for high-dimensional correlated data (2016)