Name
Abstract | ||
|---|---|---|
The stochastic variance reduced gradient (SVRG) algorithm has shown its effectiveness in accelerating the convergence of stochastic gradient algorithms. Considering the emergent applications of distributed estimation, it is interesting to investigate the way to adapt this algorithm to distributed learning with streaming data. For this purpose, in this work we first propose a time-averaging SVRG algorithm that fits into the context of streaming data processing. Then, we integrate this algorithm with the diffusion adaptation to enhance the performance of distributed estimation over networks. Theoretical analysis of the resulted algorithm is conducted to characterize its stability. We also provide the simulation results to illustrate its favorable performance. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICSPCC50002.2020.9259540 | 2020 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) |
Keywords | DocType | ISBN |
Distributed estimation,diffusion strategy,variance reduction,SVRG,stochastic optimization | Conference | 978-1-7281-7203-3 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mengfei Zhang | 1 | 2 | 3.75 |
| Danqi Jin | 2 | 1 | 2.04 |
| Jie Chen | 3 | 0 | 0.34 |