2018/02/16 by Marine Le Morvan, Morvan, Marine Le, Jean‐Philippe Vert +1 · 1 citation
Biochemistry, Genetics and Molecular Biology · #FOS: Biological sciences #FOS: Computer and information sciences #Gene expression and cancer classification #Genetic Associations and Epidemiology #Genomics and Chromatin Dynamics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Quantitative Methods (q-bio.QM)
paper · pdf · doi:10.48550/arxiv.1802.05980
openalex publication_date 2018/02/16 · openalex created_date 2018/02/23 · openalex updated_date 2026/07/28
Learning sparse linear models with two-way interactions is desirable in many application domains such as genomics. l1-regularised linear models are popular to estimate sparse models, yet standard implementations fail to address specifically the quadratic explosion of candidate two-way interactions in high dimensions, and typically do not scale to genetic data with hundreds of thousands of features. Here we present WHInter, a working set algorithm to solve large l1-regularised problems with two-way interactions for binary design matrices. The novelty of WHInter stems from a new bound to efficiently identify working sets while avoiding to scan all features, and on fast computations inspired from solutions to the maximum inner product search problem. We apply WHInter to simulated and real genetic data and show that it is more scalable and two orders of magnitude faster than the state of the art.