Support Recovery from Noisy Measurement via Orthogonal Multi-Matching Pursuit

Support Recovery from Noisy Measurement via Orthogonal Multi-Matching Pursuit

Year:    2016

Numerical Mathematics: Theory, Methods and Applications, Vol. 9 (2016), Iss. 2 : pp. 185–192

Abstract

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.

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Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.4208/nmtma.2016.m1424

Numerical Mathematics: Theory, Methods and Applications, Vol. 9 (2016), Iss. 2 : pp. 185–192

Published online:    2016-01

AMS Subject Headings:   

Copyright:    COPYRIGHT: © Global Science Press

Pages:    8

Keywords: