Dual Feature Reduction for SGL
Fit a DFR-aSGL model using k-fold cross-validation.
Fit a DFR-aSGL model.
Fit a DFR-SGL model using k-fold cross-validation.
Fit a DFR-SGL model.
dfr: Dual Feature Reduction for SGL
Plot models of the following object types: "sgl", "sgl_cv".
Predict using one of the following object types: "sgl", "sgl_cv".
Prints information for one of the following object types: "sgl", `"s...
Implementation of the Dual Feature Reduction (DFR) approach for the Sparse Group Lasso (SGL) and the Adaptive Sparse Group Lasso (aSGL) (Feser and Evangelou (2024) <doi:10.48550/arXiv.2405.17094>). The DFR approach is a feature reduction approach that applies strong screening to reduce the feature space before optimisation, leading to speed-up improvements for fitting SGL (Simon et al. (2013) <doi:10.1080/10618600.2012.681250>) and aSGL (Mendez-Civieta et al. (2020) <doi:10.1007/s11634-020-00413-8> and Poignard (2020) <doi:10.1007/s10463-018-0692-7>) models. DFR is implemented using the Adaptive Three Operator Splitting (ATOS) (Pedregosa and Gidel (2018) <doi:10.48550/arXiv.1804.02339>) algorithm, with linear and logistic SGL models supported, both of which can be fit using k-fold cross-validation. Dense and sparse input matrices are supported.