Volume 9, Issue 2
Support Recovery from Noisy Measurement via Orthogonal Multi-Matching Pursuit

Wei Dan

Numer. Math. Theor. Meth. Appl., 9 (2016), pp. 185-192.

Published online: 2016-09

Preview Purchase PDF 7 3325
Export citation
  • 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.

  • Keywords

  • AMS Subject Headings

  • Copyright

COPYRIGHT: © Global Science Press

  • Email address
  • BibTex
  • RIS
  • TXT
@Article{NMTMA-9-185, 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 = {

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.

}, issn = {2079-7338}, doi = {https://doi.org/10.4208/nmtma.2016.m1424}, url = {http://global-sci.org/intro/article_detail/nmtma/12373.html} }
TY - JOUR T1 - Support Recovery from Noisy Measurement via Orthogonal Multi-Matching Pursuit 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 -

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.

Wei Dan. (2020). Support Recovery from Noisy Measurement via Orthogonal Multi-Matching Pursuit. Numerical Mathematics: Theory, Methods and Applications. 9 (2). 185-192. doi:10.4208/nmtma.2016.m1424
Copy to clipboard
The citation has been copied to your clipboard