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dc.contributor.authorSun, John Z.
dc.date.accessioned2011-03-07T23:03:23Z
dc.date.available2011-03-07T23:03:23Z
dc.date.issued2009-07
dc.identifier.isbn978-1-4244-4312-3
dc.identifier.otherINSPEC Accession Number: 10842044
dc.identifier.urihttp://hdl.handle.net/1721.1/61623
dc.description.abstractQuantization is an important but often ignored consideration in discussions about compressed sensing. This paper studies the design of quantizers for random measurements of sparse signals that are optimal with respect to mean-squared error of the lasso reconstruction. We utilize recent results in high-resolution functional scalar quantization and homotopy continuation to approximate the optimal quantizer. Experimental results compare this quantizer to other practical designs and show a noticeable improvement in the operational distortion-rate performance.en_US
dc.description.sponsorshipNational Science Foundation (U.S.) (CAREER Award CCF-643836)en_US
dc.description.sponsorshipLincoln Laboratory. Advanced Concepts Committee (Air Force contract FA8721-05-C-0002)en_US
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/ISIT.2009.5205695en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceIEEEen_US
dc.titleOptimal quantization of random measurements in compressed sensingen_US
dc.typeArticleen_US
dc.identifier.citationSun, J.Z., and V.K. Goyal. “Optimal quantization of random measurements in compressed sensing.” Information Theory, 2009. ISIT 2009. IEEE International Symposium on. 2009. 6-10. © 2009, IEEEen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Research Laboratory of Electronicsen_US
dc.contributor.approverGoyal, Vivek K.
dc.contributor.mitauthorSun, John Z.
dc.contributor.mitauthorGoyal, Vivek K.
dc.relation.journalIEEE International Symposium on Information Theory.en_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
dspace.orderedauthorsSun, John Z.; Goyal, Vivek K.en
dspace.mitauthor.errortrue
mit.licensePUBLISHER_POLICYen_US


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