dfr-package: dfr: Dual Feature Reduction for SGL

In dfr: Dual Feature Reduction for SGL

dfr: Dual Feature Reduction for SGL

Description

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) \Sexpr[results=rd]{tools:::Rd_expr_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) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10618600.2012.681250")}) and aSGL (Mendez-Civieta et al. (2020) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s11634-020-00413-8")} and Poignard (2020) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s10463-018-0692-7")}) models. DFR is implemented using the Adaptive Three Operator Splitting (ATOS) (Pedregosa and Gidel (2018) \Sexpr[results=rd]{tools:::Rd_expr_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.

Author(s)

Maintainer: Fabio Feser [e-mail] (ORCID)

See Also

Useful links:

• https://github.com/ff1201/dfr

• Report bugs at https://github.com/ff1201/dfr/issues

dfr documentation built on April 4, 2025, 4:43 a.m.