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

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

Blog Article

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 Darkroom Equipment 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 message passing, and two other representative greedy algorithms 3 IN 1 COCONUT in solution accuracy and robustness.ASSD is especially suitable for linear regression problems with highly correlated measurement matrices encountered in real-world applications.