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An extended block restricted isometry property for sparse recovery with non-Gaussian noise
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
Department of Statistics, Zhejiang University City College, China.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics. (Mathematical Statistics)ORCID iD: 0000-0001-5673-620x
2019 (English)In: Journal of Computational Mathematics, ISSN 0254-9409, E-ISSN 1991-7139Article in journal (Refereed) Epub ahead of print
Abstract [en]

We study the recovery conditions of weighted mixed ℓ2/ℓp minimization for block sparse signal reconstruction from compressed measurements when partial block supportinformation is available. We show theoretically that the extended block restricted isometry property can ensure robust recovery when the data fidelity constraint is expressed in terms of an ℓq norm of the residual error, thus establishing a setting wherein we arenot restricted to Gaussian measurement noise. We illustrate the results with a series of numerical experiments.

Place, publisher, year, edition, pages
Global Science Press, 2019.
Keywords [en]
Compressed sensing, block sparsity, partial support information, signal reconstruction, convex optimization
National Category
Signal Processing Probability Theory and Statistics Computational Mathematics
Research subject
Mathematical Statistics
Identifiers
URN: urn:nbn:se:umu:diva-163366DOI: 10.4208/jcm.1905-m2018-0256OAI: oai:DiVA.org:umu-163366DiVA, id: diva2:1351857
Part of project
Statistical modelling and intelligent data sampling in MRI and PET measurements for cancer therapy assessment, Swedish Research Council
Funder
Swedish Research CouncilAvailable from: 2019-09-17 Created: 2019-09-17 Last updated: 2019-11-19

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Leffler, KlaraYu, Jun

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