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Author Tropp, Joel A. ♦ Needell, Deanna
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Language English
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, <code>CoSaMP</code>, that accomplishes the data recovery task. It was the first known method to offer near-optimal guarantees on resource usage.
Description Affiliation: California Institute of Technology, Pasadena, CA (Tropp, Joel A.) || Stanford University, Stanford, CA (Needell, Deanna)
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2005-08-01
Publisher Place New York
Journal Communications of the ACM (CACM)
Volume Number 53
Issue Number 12
Page Count 8
Starting Page 93
Ending Page 100

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Source: ACM Digital Library