TY - JOUR
T1 - Support Recovery from Noisy Measurement via Orthogonal Multi-Matching Pursuit
AU - Wei Dan
JO - Numerical Mathematics: Theory, Methods and Applications
VL - 2
SP - 185
EP - 192
PY - 2016
DA - 2016/09
SN - 9
DO - http://doi.org/10.4208/nmtma.2016.m1424
UR - https://global-sci.org/intro/article_detail/nmtma/12373.html
KW - 
AB - <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>