2014/10/25 by Seunghak Lee, Eric P. Xing, Lee, Seunghak +1 · 2 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Medicine · #Cancer, Lipids, and Metabolism #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistical Methods and Inference #Systemic Lupus Erythematosus Research #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1410.6880
arxiv created 2014/10/25 · openalex publication_date 2014/10/25 · arxiv updated 2014/10/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Recently, to solve large-scale lasso and group lasso problems, screening rules have been developed, the goal of which is to reduce the problem size by efficiently discarding zero coefficients using simple rules independently of the others. However, screening for overlapping group lasso remains an open challenge because the overlaps between groups make it infeasible to test each group independently. In this paper, we develop screening rules for overlapping group lasso. To address the challenge arising from groups with overlaps, we take into account overlapping groups only if they are inclusive of the group being tested, and then we derive screening rules, adopting the dual polytope projection approach. This strategy allows us to screen each group independently of each other. In our experiments, we demonstrate the efficiency of our screening rules on various datasets.