Name
Abstract | ||
|---|---|---|
Signal reconstruction over graphs arises naturally in diverse science and engineering applications. Existing methods employ either parametric or nonparametric approaches based on graph kernels. Although the former are adequate when the signals of interest adhere to postulated models, their performance degrades rapidly under model mismatch. Nonparametric alternatives on the other hand are flexible, but not as parsimonious in capturing prior information. Targeting a hybrid "sweet spot," the present contribution advocates an efficient semi-parametric approach capable of incorporating known signal structure without sacrificing the flexibility of the overall model. Numerical tests on synthetic as well as real data corroborate that the novel method leads to markedly improved signal reconstruction performance. |
Year | Venue | Keywords |
|---|---|---|
2017 | IEEE Global Conference on Signal and Information Processing | graph kernel,graph signal processing,semi-parametric inference |
Field | DocType | ISSN |
Graph kernel,Graph,Numerical tests,Computer science,Algorithm,Graph signal processing,Nonparametric statistics,Parametric statistics,Semiparametric model,Signal reconstruction | Conference | 2376-4066 |
Citations | PageRank | References |
1 | 0.37 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vassilis N. Ioannidis | 1 | 14 | 7.34 |
| Athanasios N. Nikolakopoulos | 2 | 59 | 9.02 |
| Georgios B. Giannakis | 3 | 4977 | 340.58 |