Name
Title | ||
|---|---|---|
Sparse Diffusion Steepest-Descent for One Bit Compressed Sensing in Wireless Sensor Networks |
Abstract | ||
|---|---|---|
This letter proposes a sparse diffusion steepest-descent algorithm for one bit compressed sensing in wireless sensor networks. The approach exploits the diffusion strategy from distributed learning in the one bit compressed sensing framework. To estimate a common sparse vector cooperatively from only the sign of measurements, steepest-descent is used to minimize the suitable global and local convex cost functions. A diffusion strategy is suggested for distributive learning of the sparse vector. Simulation results show the effectiveness of the proposed distributed algorithm compared to the state-of-the-art non distributive algorithms in the one bit compressed sensing framework. |
Year | Venue | Field |
|---|---|---|
2016 | CoRR | Distributive property,Mathematical optimization,Gradient descent,Computer science,Distributed learning,Regular polygon,Distributed algorithm,Artificial intelligence,Wireless sensor network,Compressed sensing,Machine learning |
DocType | Volume | Citations |
Journal | abs/1601.00350 | 0 |
PageRank | References | Authors |
0.34 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hadi. Zayyani | 1 | 96 | 15.51 |
| Mehdi Korki | 2 | 46 | 5.98 |
| Farrokh Marvasti | 3 | 113 | 13.55 |