CoSaMP: Iterative Signal Recovery from Incomplete and Inaccurate Samples.
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 i...
| Publicado en: | Communications of the ACM Vol. 53; no. 12; pp. 93 - 101 |
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| Autores principales: | , |
| Formato: | Artículo |
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Association for Computing Machinery
Dec2010
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=55618664&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 55618664 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00010782 ACM jtl: Communications of the ACM issn: 00010782 maglogo: N pubinfo: dt: Dec2010 vid: 53 iid: 12 pid: 68 pub: Association for Computing Machinery artinfo: ui: 55618664 10.1145/1859204.1859229 ppf: 93 ppct: 8 formats: tig: atl: CoSaMP: Iterative Signal Recovery from Incomplete and Inaccurate Samples. aug: au: Needell, Deanna Tropp, Joel A. affil: Stanford University, Stanford, CA. California Institute of Technology, Pasadena, CA. su: Digital signal processing Computer programming Signal processing Statistical sampling Sampling (Process) Computer science sug: subj: Digital signal processing Computer programming Signal processing Statistical sampling Sampling (Process) Computer science ab: 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. pubtype: Periodical doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2010 holdings: @attributes: islocal: N |
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