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Recovery analysis for weighted mixed l_2/l_p minimization with 0 < p <= 1
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 conditions of weighted mixed l_2/l_p (0 < p ≤ 1) minimization for blocksparse signal reconstruction from compressed measurements when partial block support information isavailable. We show that the block p-restricted isometry property (RIP) can ensure the robust recovery.Moreover, we present the sufficient and necessary condition for the recovery by using weighted block p-nullspace property. The relationship between the block p-RIP and the weighted block p-null space propertyhas been established. Finally, we illustrate our results with a series of numerical experiments.

Place, publisher, year, edition, pages
2017. , p. 12
Keyword [en]
Compressive sensing; Prior support information; Block sparse; Non-convex minimization
National Category
Probability Theory and Statistics Computational Mathematics
Research subject
Mathematical Statistics
Identifiers
URN: urn:nbn:se:umu:diva-141543OAI: oai:DiVA.org:umu-141543DiVA, id: diva2:1155309
Projects
Statistical modelling and intelligent data sampling in MRI and PET measurements for cancer therapy assessment
Funder
Swedish Research Council, 340-2013-5342
Available from: 2017-11-07 Created: 2017-11-07 Last updated: 2018-06-09

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arXiv:1709.00257

Authority records BETA

Zhou, ZhiyongYu, Jun

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CiteExportLink to record
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Citation style
  • apa
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  • asciidoc
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