Approximate Message Passing Algorithm for Nonconvex Regularization

被引：0

作者：

Zhang, Hai ^{[1
]}

Ma, Yan ^{[1
]}

Zhang, Hui ^{[1
]}

Wang, Puyu ^{[1
]}

Wang, Shcnghan ^{[1
]}

Meng, Wenhui ^{[1
]}

机构：

[1] Northwest Univ, Sch Math, Xian 710069, Shaanxi, Peoples R China

来源：

2017 IEEE 7TH ANNUAL INTERNATIONAL CONFERENCE ON CYBER TECHNOLOGY IN AUTOMATION, CONTROL, AND INTELLIGENT SYSTEMS (CYBER) | 2017年

关键词：

UNCERTAINTY PRINCIPLES; VARIABLE SELECTION; REPRESENTATION;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We study the approximate message passing algorithm for nonconvex regularization methods. We propose some improved nonconvex iterative thresholding algorithms based on approximate message passing. The new iterative thresholding algorithms are inspired by belief propagation in graphical models. Further, we study the convergence of the new algorithms and provide a series of experiments to assess the performance of the new algorithms. The experiments show that several important nonconvex iterative thresholding algorithms based on approximate message passing have strong reconstruction capabilities and high phase transition for sparse signal recovery.

引用

页码：1615 / 1620

页数：6

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