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Volume 37, Issue 3
Weighted $\ell_p$-Minimization for Sparse Signal Recovery under Arbitrary Support Prior

Yueqi Ge, Wengu Chen, Huanmin Ge & Yaling Li

DOI: 10.4208/ata.2021.lu80.02

Anal. Theory Appl., 37 (2021), pp. 289-310.

Published online: 2021-09

[An open-access article; the PDF is free to any online user.]

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  • Abstract

Weighted $\ell_p$ ($0<p\leq1$) minimization has been extensively studied as an effective way to reconstruct a sparse signal from compressively sampled measurements when some prior support information of the signal is available. In this paper, we consider the recovery guarantees of $k$-sparse signals via the weighted $\ell_p$ ($0<p\leq1$) minimization when arbitrarily many support priors are given. Our analysis enables an extension to existing works that assume only a single support prior is used.

  • Keywords

Adaptive recovery, compressed sensing, weighted $\ell_p$ minimization, sparse representation, restricted isometry property.

  • AMS Subject Headings

90C26, 90C30, 94A20

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{ATA-37-289, author = {Ge , YueqiChen , WenguGe , Huanmin and Li , Yaling}, title = {Weighted $\ell_p$-Minimization for Sparse Signal Recovery under Arbitrary Support Prior}, journal = {Analysis in Theory and Applications}, year = {2021}, volume = {37}, number = {3}, pages = {289--310}, abstract = {

Weighted $\ell_p$ ($0<p\leq1$) minimization has been extensively studied as an effective way to reconstruct a sparse signal from compressively sampled measurements when some prior support information of the signal is available. In this paper, we consider the recovery guarantees of $k$-sparse signals via the weighted $\ell_p$ ($0<p\leq1$) minimization when arbitrarily many support priors are given. Our analysis enables an extension to existing works that assume only a single support prior is used.

}, issn = {1573-8175}, doi = {https://doi.org/10.4208/ata.2021.lu80.02}, url = {http://global-sci.org/intro/article_detail/ata/19876.html} }
TY - JOUR T1 - Weighted $\ell_p$-Minimization for Sparse Signal Recovery under Arbitrary Support Prior AU - Ge , Yueqi AU - Chen , Wengu AU - Ge , Huanmin AU - Li , Yaling JO - Analysis in Theory and Applications VL - 3 SP - 289 EP - 310 PY - 2021 DA - 2021/09 SN - 37 DO - http://doi.org/10.4208/ata.2021.lu80.02 UR - https://global-sci.org/intro/article_detail/ata/19876.html KW - Adaptive recovery, compressed sensing, weighted $\ell_p$ minimization, sparse representation, restricted isometry property. AB -

Weighted $\ell_p$ ($0<p\leq1$) minimization has been extensively studied as an effective way to reconstruct a sparse signal from compressively sampled measurements when some prior support information of the signal is available. In this paper, we consider the recovery guarantees of $k$-sparse signals via the weighted $\ell_p$ ($0<p\leq1$) minimization when arbitrarily many support priors are given. Our analysis enables an extension to existing works that assume only a single support prior is used.

Ge , YueqiChen , WenguGe , Huanmin and Li , Yaling. (2021). Weighted $\ell_p$-Minimization for Sparse Signal Recovery under Arbitrary Support Prior. Analysis in Theory and Applications. 37 (3). 289-310. doi:10.4208/ata.2021.lu80.02
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