Approximate Message Passing Algorithm for Nonconvex Regularization

被引：4
|
作者：
Zhang, Hui [1 ]
Zhang, Hai [1 ,2 ,3 ]
Liang, Yong [1 ,2 ]
Yang, Zi-Yi [1 ]
Ren, Yanqiong [1 ]
机构：
[1] Macau Univ Sci & Technol, Fac Informat Technol, Macau 519020, Peoples R China
[2] Macau Univ Sci & Technol, State Key Lab Qual Res Chinese Med, Macau 519020, Peoples R China
[3] Northwest Univ, Sch Math, Xian 710127, Shaanxi, Peoples R China
来源：
IEEE ACCESS | 2019年 / 7卷
基金：
中国国家自然科学基金;
关键词：
Approximate message passing algorithm; iterative thresholding algorithm; nonconvex regularization; sparsity; variable selection; UNCERTAINTY PRINCIPLES; SELECTION; REPRESENTATION;
D O I：
10.1109/ACCESS.2019.2891121
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, we study the sparse signal reconstruction with nonconvex regularization, mainly focusing on two popular nonconvex regularization methods, minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD). An approximate message passing (AMP) algorithm is an effective method for signal reconstruction. Based on the AMP algorithm, we propose an improved MCP iterative thresholding algorithm and an improved SCAD iterative thresholding algorithm. Furthermore, we analyze the convergence of the new algorithms and provide a series of experiments to assess the performance of the new algorithms. The experiments show that the new algorithms based on AMP have stronger reconstruction capabilities, higher phase transition for sparse signal reconstruction, and better variable selection ability than the original MCP iterative thresholding algorithm and the original SCAD iterative thresholding algorithm.
引用
收藏
页码：9080 / 9090
页数：11
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