2012/05/06 by Vincent, Martin, Niels Richard Hansen, Hansen, Niels Richard · 2 citations
Engineering · Immunology and Microbiology · Medicine · #Computation (stat.CO) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Sparse and Compressive Sensing Techniques #Systemic Lupus Erythematosus Research #interferon and immune responses
paper · pdf · doi:10.48550/arxiv.1205.1245
openalex publication_date 2012/05/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The sparse group lasso optimization problem is solved using a coordinate gradient descent algorithm. The algorithm is applicable to a broad class of convex loss functions. Convergence of the algorithm is established, and the algorithm is used to investigate the performance of the multinomial sparse group lasso classifier. On three different real data examples the multinomial group lasso clearly outperforms multinomial lasso in terms of achieved classification error rate and in terms of including fewer features for the classification. The run-time of our sparse group lasso implementation is of the same order of magnitude as the multinomial lasso algorithm implemented in the R package glmnet. Our implementation scales well with the problem size. One of the high dimensional examples considered is a 50 class classification problem with 10k features, which amounts to estimating 500k parameters. The implementation is available as the R package msgl.