Name
Abstract | ||
|---|---|---|
Sparse signal reconstruction can be regarded as a problem of locating the nonzero entries of the signal. In presence of measurement noise, conventional methods such as l1 norm relaxation methods and greedy algorithms, have shown their weakness in finding the nonzero entries accurately. In order to reduce the impact of noise and better locate the nonzero entries, in this paper, we propose a two-pha... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TCYB.2017.2679705 | IEEE Transactions on Cybernetics |
Keywords | Field | DocType |
Greedy algorithms,Feature extraction,Image reconstruction,Sensors,Cost function,Linear programming | Iterative reconstruction,Least squares,Mathematical optimization,Evolutionary algorithm,Relaxation (iterative method),Greedy algorithm,Artificial intelligence,Cluster analysis,Machine learning,Signal reconstruction,Compressed sensing,Mathematics | Journal |
Volume | Issue | ISSN |
47 | 9 | 2168-2267 |
Citations | PageRank | References |
8 | 0.45 | 27 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Zhou | 1 | 58 | 4.86 |
| Sam Kwong | 2 | 4590 | 315.78 |
| Hainan Guo | 3 | 8 | 1.13 |
| xiao | 4 | 78 | 7.06 |
| Qingfu Zhang | 5 | 7634 | 255.05 |