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Stable and robust l_p-constrained compressive sensing recovery via robust width property
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics. (Mathematical Statistics)
2017 (English)Manuscript (preprint) (Other academic)
Abstract [en]

We study the recovery results of l_p-constrained compressive sensing (CS) with p ≥ 1 via robustwidth property and determine conditions on the number of measurements for standard Gaussian matricesunder which the property holds with high probability. Our paper extends the existing results in Cahilland Mixon (2014) from l_2-constrained CS to ℓp-constrained case with p ≥ 1 and complements the recoveryanalysis for robust CS with l_p loss function.

Place, publisher, year, edition, pages
2017. , p. 12
Keywords [en]
Compressive sensing; Robust width property; Robust null space property; Restricted isometry property.
National Category
Probability Theory and Statistics Computational Mathematics
Research subject
Mathematical Statistics
Identifiers
URN: urn:nbn:se:umu:diva-141545OAI: oai:DiVA.org:umu-141545DiVA, id: diva2:1155311
Projects
Statistical modelling and intelligent data sampling in MRI and PET measurements for cancer therapy assessment
Funder
Swedish Research Council, 340-2013-5342Available from: 2017-11-07 Created: 2017-11-07 Last updated: 2018-06-09

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arXiv:1705.03810

Authority records BETA

Zhou, ZhiyongYu, Jun

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CiteExportLink to record
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