@Article{NMTMA-9-2,
author = {},
title = {Support Recovery from Noisy Measurement via Orthogonal Multi-Matching Pursuit},
journal = {Numerical Mathematics: Theory, Methods and Applications},
year = {2016},
volume = {9},
number = {2},
pages = {185--192},
abstract = {<p style="text-align: justify;">In this paper, a new stopping rule is proposed for orthogonal multi-matching
pursuit (OMMP). We show that, for $ℓ_2$ bounded noise case, OMMP with the new
stopping rule can recover the true support of any $K$-sparse signal $x$ from noisy
measurements $y = Φx + e$ in at most $K$ iterations, provided that all the nonzero
components of $x$ and the elements of the matrix $Φ$ satisfy certain requirements. The
proposed method can improve the existing result. In particular, for the noiseless
case, OMMP can exactly recover any $K$-sparse signal under the same RIP condition.</p>},
issn = {2079-7338},
doi = {https://doi.org/10.4208/nmtma.2016.m1424},
url = {https://global-sci.com/article/90538/support-recovery-from-noisy-measurement-via-orthogonal-multi-matching-pursuit}
}