Name
Abstract | ||
|---|---|---|
This work develops a variation of diffusion learning by incorporating an adaptive construction for the combination weights through local fusion steps. This leads to an implementation with enhanced convergence rate and mean-square error performance while maintaining, the same level of complexity as standard implementations. The approach is based on formulating optimal or close-to-optimal learning and fusion steps using a proximity function rationale within neighborhoods. The first version of the algorithm employs exact fusion in the least-squares sense using inverses of uncertainty matrices. The second version replaces these matrices by diagonal approximations with reduced complexity. The result is an LMS-complexity scheme with improved performance for distributed learning over networks. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.23919/EUSIPCO.2019.8902953 | 2019 27TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO) |
Keywords | Field | DocType |
diffusion networks, fusion, least-squares, adaptation, combination weights | Diagonal,Least squares,Matrix (mathematics),Computer science,Fusion,Algorithm,Distributed learning,Implementation,Rate of convergence | Conference |
ISSN | Citations | PageRank |
2076-1465 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ricardo Merched | 1 | 35 | 9.98 |
| Stefan Vlaski | 2 | 23 | 11.39 |
| Ali H. Sayed | 3 | 9134 | 667.71 |