Critical Compression Ratio of Iterative Reweighted l1 Minimization for Compressed Sensing

被引：0

作者：

Matsushita, Ryosuke ^{[1
]}

Tanaka, Toshiyuki ^{[1
]}

机构：

[1] Kyoto Univ, Grad Sch Informat, Sakyo Ku, Kyoto 6068501, Japan

来源：

2011 IEEE INFORMATION THEORY WORKSHOP (ITW) | 2011年

关键词：

NEIGHBORLINESS;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

l(1) minimization for compressed sensing provides a computationally efficient means to reconstruct sparse signals from linear measurements whose number is less than the dimension of the signal. Reconstruction from a smaller number of measurements can be possible via iterative reweighted l(1) minimization (IRL1). In this paper, adopting a statistical-mechanics approach, we propose an analytical framework for evaluating critical compression ratio, the ratio of the number of measurements to the dimension of the signal, for IRL1.

引用

页数：5

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