Name
Abstract | ||
|---|---|---|
In this paper we address the recovery conditions of weighted $\ell_p$ minimization for signal reconstruction from compressed sensing measurements when partial support information is available. We show that weighted $\ell_p$ minimization with $0<p<1$ is stable and robust under weaker sufficient conditions compared to weighted $\ell_1$ minimization. Moreover, the sufficient recovery conditions of weighted $\ell_p$ are weaker than those of regular $\ell_p$ minimization if at least $50%$ of the support estimate is accurate. We also review some algorithms which exist to solve the non-convex $\ell_p$ problem and illustrate our results with numerical experiments. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/BF03549582 | CoRR |
Field | DocType | Volume |
Mathematical optimization,Regular polygon,Minification,Compressed sensing,Signal reconstruction,Mathematics | Journal | abs/1311.3773 |
Issue | Citations | PageRank |
3 | 1 | 0.41 |
References | Authors | |
5 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Navid Ghadermarzy | 1 | 10 | 1.94 |
| Hassan Mansour | 2 | 349 | 34.12 |
| Özgür Yilmaz | 3 | 685 | 51.36 |