dfr - Dual Feature Reduction for SGL
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.
