CoSaMP: iterative signal recovery from incomplete and inaccurate samples

CoSaMP: iterative signal recovery from incomplete and inaccurate samples CoSaMP: Iterative Signal Recovery from Incomplete and Inaccurate Samples By Deanna Needell and Joel A. Tropp abstract 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. 1. WhaT iS ComPRESSiVE SamPLinG? In many applications, the ambient dimension of a data vector does not reflect the actual number of degrees of freedom in that vector. In mathematical signal processing, this property is captured by the notion of a compressible signal. Natural images provide a concrete example of compressible signals because we can approximate them accurately just by summarizing the solid areas (local averages) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Communications of the ACM Association for Computing Machinery

CoSaMP: iterative signal recovery from incomplete and inaccurate samples

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2010 by ACM Inc.
ISSN
0001-0782
D.O.I.
10.1145/1859204.1859229
Publisher site
See Article on Publisher Site

Abstract

CoSaMP: Iterative Signal Recovery from Incomplete and Inaccurate Samples By Deanna Needell and Joel A. Tropp abstract 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. 1. WhaT iS ComPRESSiVE SamPLinG? In many applications, the ambient dimension of a data vector does not reflect the actual number of degrees of freedom in that vector. In mathematical signal processing, this property is captured by the notion of a compressible signal. Natural images provide a concrete example of compressible signals because we can approximate them accurately just by summarizing the solid areas (local averages)

Journal

Communications of the ACMAssociation for Computing Machinery

Published: Dec 1, 2010

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