An adaptive shortest-solution guided decimation approach to sparse high-dimensional linear regression.

ABSTRACT: High-dimensional linear regression model is the most popular statistical model for high-dimensional data, but it is quite a challenging task to achieve a sparse set of regression coefficients. In this paper, we propose a simple heuristic algorithm to construct sparse high-dimensional linear regression models, which is adapted from the shortest-solution guided decimation algorithm and is referred to as ASSD. This algorithm constructs the support of regression coefficients under the guidance of the shortest least-squares solution of the recursively decimated linear models, and it applies an early-stopping criterion and a second-stage thresholding procedure to refine this support. Our extensive numerical results demonstrate that ASSD outperforms LASSO, adaptive LASSO, vector approximate messa

SUBMITTER: Yu X 

PROVIDER: S-EPMC8674299 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature