L0范数平滑逼近的稳健求解算法

doi:10.11999/JEIT141590

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Abstract

Computational framework using surrogate functions and prior probability density functions, for smoothed L0 minimization approximation is studied in this paper, for the purpose of improving the recovery performance of non-convex compressed sensing. Firstly, a simple parameter adjusting strategy and modified SL0 and FOCUSS are presented, based on the convex-concave property analysis of approximation functions. Secondly, since L0 approximation problem can be viewed as a L0-Regularized Least Squares problem in noisy setting，a new computational framework called IRSL0 (Iteratively Reweighted SL0) is derived from the Newton direction, furthermore, a new surrogate function is also given. Finally, extensive numerical simulations demonstrate the robustness and applicability of the new theory and algorithms.

Key words： Non-convex compressed sensing Smoothed L0 approximation Iteratively reweighted algorithms

Received: 11 December 2014 Published: 06 July 2015

PACS:

TN911.72

Fund:

The National Natural Science Foundation of China (61379104); The Natural Science Foundation of Shaanxi Province (2014JM2-6106)

Corresponding Authors: Wang Feng E-mail: wangfengisn@163.com

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	Wang Feng
	Xiang Xin
	Yi Ke-chu
	Xiong Lei

Cite this article:

Wang Feng,Xiang Xin,Yi Ke-chu等. Robust Computational Methods for Smoothed L0 Approximation[J]. JEIT, 2015, 37(10): 2377-2382.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT141590 OR http://jeit.ie.ac.cn/EN/Y2015/V37/I10/2377

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