An Alps View of Sparse Recovery

We provide two compressive sensing (CS) recovery algorithms based on iterative hard-thresholding. The algorithms, collectively dubbed as algebraic pursuits (ALPS), exploit the restricted isometry properties of the CS measurement matrix within the algebra of Nesterov's optimal gradient methods. We theoretically characterize the approximation guarantees of ALPS for signals that are sparse on ortho-bases as well as on tight-frames. Simulation results demonstrate a great potential for ALPS in terms of phase-transition, noise robustness, and CS reconstruction.


Published in:
Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 5808-5811
Presented at:
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Prague, Czech Republic, May 22-27, 2011
Year:
2011
Publisher:
Ieee Service Center, 445 Hoes Lane, Po Box 1331, Piscataway, Nj 08855-1331 Usa
ISBN:
978-1-4577-0538-0
Laboratories:




 Record created 2011-12-29, last modified 2018-01-28

External link:
Download fulltext
n/a
Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)