@Article{ATA-37-3,
author = {Yueqi, Ge and Wengu, Chen and Huanmin, Ge and Yaling, Li},
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 = {<p style="text-align: justify;">Weighted $\ell_p$ ($0&lt;p\leq1$) 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 $\ell_p$ ($0&lt;p\leq1$) 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.</p>},
issn = {1573-8175},
doi = {https://doi.org/10.4208/ata.2021.lu80.02},
url = {https://global-sci.com/article/73783/weighted-ell-p-minimization-for-sparse-signal-recovery-under-arbitrary-support-prior}
}