Name
Abstract | ||
|---|---|---|
•By combining the concept of weak convexity with l1 norm loss function, a robust sparse recovery framework for impulsive noise is proposed and theoretically analyzed.•Model analysis guarantees that this novel robust sparse recovery formulation guarantees to attain the global optimum.•An efficient algorithm based on ADMM is developed to solve the corresponding nonconvex and nonsmooth minimization. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.sigpro.2018.05.020 | Signal Processing |
Keywords | Field | DocType |
Weakly convex optimization,Robust sparse recovery,ADMM,Global optimum | Residual,Mathematical optimization,Slack variable,Convexity,Outlier,Minification,Convex function,Convex optimization,Optimization problem,Mathematics | Journal |
Volume | ISSN | Citations |
152 | 0165-1684 | 3 |
PageRank | References | Authors |
0.41 | 14 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qi Liu | 1 | 115 | 17.85 |
| Chengzhu Yang | 2 | 4 | 1.10 |
| Yuantao Gu | 3 | 752 | 73.88 |
| H.C. So | 4 | 540 | 46.62 |