2015/11/13 by William Herlands, Herlands, William, Maria De-Arteaga +7
Engineering · Mathematics · #Advanced Statistical Methods and Models #FOS: Computer and information sciences #Machine Learning (stat.ML) #Sparse and Compressive Sensing Techniques #Statistical Methods and Inference #stat.ML
paper · pdf · doi:10.48550/arxiv.1511.04402
8 pages, 1 figure. NIPS 2015 Workshop of Optimization (OPT2015)
openalex publication_date 2015/11/13 · arxiv created 2016/02/17 · arxiv updated 2016/02/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We compute approximate solutions to L0 regularized linear regression using L1 regularization, also known as the Lasso, as an initialization step. Our algorithm, the Lass-0 ("Lass-zero"), uses a computationally efficient stepwise search to determine a locally optimal L0 solution given any L1 regularization solution. We present theoretical results of consistency under orthogonality and appropriate handling of redundant features. Empirically, we use synthetic data to demonstrate that Lass-0 solutions are closer to the true sparse support than L1 regularization models. Additionally, in real-world data Lass-0 finds more parsimonious solutions than L1 regularization while maintaining similar predictive accuracy.