GAP Safe Screening Rules for Sparse-Group-Lasso

Author name not available (Why is that?)

Publication date: 19 February 2016

Abstract: In high dimensional settings, sparse structures are crucial for efficiency, either in term of memory, computation or performance. In some contexts, it is natural to handle more refined structures than pure sparsity, such as for instance group sparsity. Sparse-Group Lasso has recently been introduced in the context of linear regression to enforce sparsity both at the feature level and at the group level. We adapt to the case of Sparse-Group Lasso recent safe screening rules that discard early in the solver irrelevant features/groups. Such rules have led to important speed-ups for a wide range of iterative methods. Thanks to dual gap computations, we provide new safe screening rules for Sparse-Group Lasso and show significant gains in term of computing time for a coordinate descent implementation.

Has companion code repository: https://github.com/EugeneNdiaye/GAPSAFE_SGL

This page was built for publication: GAP Safe Screening Rules for Sparse-Group-Lasso

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6270649)