The Performance Analysis of Extending Iterative Reweighted Least  Squares Algorithm Compressed Sensing Theory

CHEN Xiaoling; ZHAO Huimin; WEI Wenguo

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The Performance Analysis of Extending Iterative Reweighted Least Squares Algorithm Compressed Sensing Theory

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- The Performance Analysis of Extending Iterative Reweighted Least Squares Algorithm Compressed Sensing Theory
- Acta Scientiarum Naturalium Universitatis SunYatseni Vol. 53, Issue 2, Pages: 23-28(2014)
- 作者机构：
  
  广东技术师范学院电子与信息学院,广东,广州,510665
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2014，
  
  Published Online：25 March 2014，
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CHEN Xiaoling, ZHAO Huimin, WEI Wenguo. The Performance Analysis of Extending Iterative Reweighted Least Squares Algorithm Compressed Sensing Theory. [J]. Acta Scientiarum Naturalium Universitatis SunYatseni 53(2):23-28(2014)
DOI：

CHEN Xiaoling, ZHAO Huimin, WEI Wenguo. The Performance Analysis of Extending Iterative Reweighted Least Squares Algorithm Compressed Sensing Theory. [J]. Acta Scientiarum Naturalium Universitatis SunYatseni 53(2):23-28(2014) DOI：

摘要

利用最稀疏表示重构原始信号是压缩感知理论的核心，而基于几何影射约束的最小 l

范数凸优化算法是其实现的主要方法。目前，解决最小 l

(p≤1) 范数问题的关键是迭代重加权最小二乘算法（IRLS

－p

0

p≤1)，但其收敛和实时性较差。为此，文中从最小化矩阵秩的角度出发对一类扩展迭代重加权最小二乘算法（EIRLS

－p

）进行性能实现分析，用以改进 IRLS

－p

算法的连续迭代收敛性及其实时性能。验证结果表明，EIRLS

－0

和 sEIRLS

－0

算法性能优于奇异值门限(SVT)算法。同时

在没有先验知识的情况下

sEIRLS

－0

算法性能也优于迭代硬阈值(IHT)算法。

Abstract

The kernel technology of Compressed sensing theory is to find the sparsest representation to recover original signal data

in which the convex optimization algorithm of minimization the l

norm is a important method. At present

a key algorithm solved minimization the l

(p≤1)norm is iterative reweighted least squares algorithm（IRLS

－p

p≤1) with affine constraints

but a crucial question of the IRLS

－p

Algorithm is to iterate convergence and real time performances. Therefore

the EIRLS

－p

and sEIRLS

－p

algorithms were proposed to extend IRLS

－p

as a family of algorithms for the matrix rank minimization problem

and to improve IRLS

－p

implementations performances of successive iterates convergence and real time. Validating results show that both EIRLS

－0

and sEIRLS

－0

perform better than singular value thresholding (SVT) algorithm. At the same time

it was observed that sEIRLS

－0

performs better than iterative hard thresholding algorithm(IHT) when there is no apriori information on the low rank solution.

关键词

迭代重加权矩阵秩压缩感知Frobenius范数

Keywords

iterative reweightedmatrix rankcompressed sensingFrobenius norm

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Related Institution

College of Electronic and Information Engineering,Foshan University

Industrial Training Center,Guangdong Polytechnic Normal University

Department of Computer, Foshan University, Foshan



佛山科学技术学院计算机系，广东佛山 528000

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