Name
Abstract | ||
|---|---|---|
This work proposes a family of greedy algorithms to jointly reconstruct a set of vectors that are (i) nonnegative and (ii) simultaneously sparse with a shared support set. The proposed algorithms generalize previous approaches that were designed to impose these constraints individually. Similar to previous greedy algorithms for sparse recovery, the proposed algorithms iteratively identify promising support indices. In contrast to previous approaches, the support index selection procedure has been adapted to prioritize indices that are consistent with both the nonnegativity and shared support constraints. Empirical results demonstrate for the first time that the combined use of simultaneous sparsity and nonnegativity constraints can substantially improve recovery performance relative to existing greedy algorithms that impose less signal structure. HighlightsWe propose a new family of greedy algorithms for reconstructing structured signals.The algorithms assume that signals are nonnegative and simultaneously sparse.The algorithms are demonstrated to have performance advantages in empirical tests.Theory is presented to complement the algorithmic/empirical contributions. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.sigpro.2016.01.021 | Signal Processing |
Keywords | Field | DocType |
Compressed Sensing,Greedy Algorithms,Nonnegativity,Simultaneous sparsity | Mathematical optimization,Computer science,Algorithm,Greedy algorithm,Index selection,Greedy randomized adaptive search procedure,Compressed sensing | Journal |
Volume | Issue | ISSN |
125 | C | 0165-1684 |
Citations | PageRank | References |
2 | 0.36 | 27 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daeun Kim | 1 | 8 | 1.48 |
| Justin P. Haldar | 2 | 350 | 35.40 |