Sequential sparse matching pursuit
Name
Berinde-2009-Sequential sparse matching pursuit.pdf
Size
320.16 KB
Format
Adobe PDF
Checksum (MD5)
21f96f28764ae3ab89aa011d7e98ae60
Author(s)
Berinde, Radu
Indyk, Piotr
Date Issued
September 2009
Journal
Allerton Conference on Communication, Control, and Computing
Publisher
Institute of Electrical and Electronics Engineers
Citation
Berinde, R., and P. Indyk. “Sequential Sparse Matching Pursuit.” Communication, Control, and Computing, 2009. Allerton 2009. 47th Annual Allerton Conference on. 2009. 36-43. © 2009, IEEE
Version
Final published version
Abstract
We propose a new algorithm, called sequential sparse matching pursuit (SSMP), for solving sparse recovery problems. The algorithm provably recovers a k-sparse approximation to an arbitrary n-dimensional signal vector x from only O(k log(n/k)) linear measurements of x. The recovery process takes time that is only near-linear in n. Preliminary experiments indicate that the algorithm works well on synthetic and image data, with the recovery quality often outperforming that of more complex algorithms, such as à ¿1 minimization.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ALLERTON.2009.5394834
