Name
Abstract | ||
|---|---|---|
Neural networks that are embedded with prior knowledge of the distribution of the original signal in applications of compressed sensing have attracted increasing attention. However, the maximal probability of the desired output by a neural network cannot guarantee that the statistical distribution of the recovered signal is consistent with the statistical distribution of the original signal. In this paper, we combine neural networks with information geometry to study the recovery of sparse signals that satisfy a certain distribution. We construct the geodesic distance between the distribution of the original signal and distribution of the recovered signal as the input for the neural network. Experiments show that the proposed method has a better reconstruction quality compared with existing algorithms. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s00034-018-0869-6 | Circuits, Systems, and Signal Processing |
Keywords | Field | DocType |
Sparse recovery,Information geometry,Geodesic distance,Neural network | Information geometry,Mathematical optimization,Algorithm,Artificial neural network,Compressed sensing,Mathematics,Geodesic | Journal |
Volume | Issue | ISSN |
38 | 2 | 1531-5878 |
Citations | PageRank | References |
1 | 0.36 | 28 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meng Wang | 1 | 2 | 0.70 |
| Chuangbai Xiao | 2 | 40 | 16.05 |
| Zhen-Hu Ning | 3 | 7 | 5.51 |
| Tong Li | 4 | 148 | 30.10 |
| Bei Gong | 5 | 1 | 0.36 |