CoSaMP: Iterative Signal Recovery from Incomplete and Inaccurate Samples

被引：261

作者：

Needell, Deanna ^{[1
]}

Tropp, Joel A. ^{[2
]}

机构：

[1] Stanford Univ, Stanford, CA 94305 USA

[2] CALTECH, Pasadena, CA 91125 USA

来源：

COMMUNICATIONS OF THE ACM | 2010年 / 53卷 / 12期

关键词：

UNCERTAINTY PRINCIPLES;

D O I：

10.1145/1859204.1859229

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Compressive sampling (CoSa) is a new paradigm for developing data sampling technologies. It is based on the principle that many types of vector-space data are compressible, which is a term of art in mathematical signal processing. The key ideas are that randomized dimension reduction preserves the information in a compressible signal and that it is possible to develop hardware devices that implement this dimension reduction efficiently. The main computational challenge in CoSa is to reconstruct a compressible signal from the reduced representation acquired by the sampling device. This extended abstract describes a recent algorithm, called CoSaMP, that accomplishes the data recovery task. It was the first known method to offer near-optimal guarantees on resource usage.

引用

页码：93 / 100

页数：8

共 24 条

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[2]

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