Name
Abstract | ||
|---|---|---|
Algorithms to construct/recover low-rank matrices satisfying a set of linear equality constraints have important applications in many signal processing contexts. Recently, theoretical guarantees for minimum-rank matrix recovery have been proven for nuclear norm minimization (NNM), which can be solved using standard convex optimization approaches. While nuclear norm minimization is effective, it ca... |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/LSP.2009.2018223 | IEEE Signal Processing Letters |
Keywords | Field | DocType |
Equations,Signal processing algorithms,Vectors,Compressed sensing,Heuristic algorithms,Computational efficiency,Optimization methods,Robustness,Constraint theory | Signal processing,Mathematical optimization,Nuclear norm minimization,Matrix (mathematics),Constraint theory,Robustness (computer science),Convex optimization,Mathematics,Compressed sensing,Signal processing algorithms | Journal |
Volume | Issue | ISSN |
16 | 7 | 1070-9908 |
Citations | PageRank | References |
56 | 3.17 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Justin P. Haldar | 1 | 350 | 35.40 |
| Diego Hernando | 2 | 122 | 7.94 |