Name
Abstract | ||
|---|---|---|
•Proposed the infimal convolution smoothing technique to approximate the non-differentiable loss function.•Introduced a relaxation factor to transform the non-differentiable loss function into a smooth counterpart.•Provided convergence analysis for the algorithm under the APG and mAPG framework.•Evaluated the effectiveness of the proposed algorithm via numerical experiments. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.neucom.2019.08.035 | Neurocomputing |
Keywords | Field | DocType |
Robust sparse recovery,Impulsive noise,Smoothing method,Proximal gradient,Non-convex regularization | Convergence (routing),Residual,Convolution,Algorithm,Outlier,Smoothing,Regularization (mathematics),Lipschitz continuity,Monotone polygon,Mathematics | Journal |
Volume | ISSN | Citations |
371 | 0925-2312 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| yuli sun 孙玉立 | 1 | 7 | 6.26 |
| Lin Lei | 2 | 5 | 4.56 |
| Xiao Li | 3 | 0 | 1.69 |
| Ming Li | 4 | 13 | 4.67 |
| Gangyao Kuang | 5 | 347 | 31.11 |