Name
Title | ||
|---|---|---|
Joint Design of Measurement Matrix and Sparse Support Recovery Method via Deep Auto-Encoder. |
Abstract | ||
|---|---|---|
Sparse support recovery arises in many applications in communications and signal processing. Existing methods tackle sparse support recovery problems for a given measurement matrix, and cannot flexibly exploit the properties of sparsity patterns for improving performance. In this letter, we propose a data-driven approach to jointly design the measurement matrix and support recovery method for comp... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/LSP.2019.2945683 | IEEE Signal Processing Letters |
Keywords | DocType | Volume |
Sparse matrices,Neurons,Noise measurement,Training,Deep learning,Computer architecture,Computational complexity | Journal | 26 |
Issue | ISSN | Citations |
12 | 1070-9908 | 4 |
PageRank | References | Authors |
0.40 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuaichao Li | 1 | 14 | 0.88 |
| Wanqing Zhang | 2 | 14 | 0.88 |
| Ying Cui | 3 | 46 | 6.92 |
| Hei Victor Cheng | 4 | 70 | 10.01 |
| Wei Yu | 5 | 324 | 22.95 |