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. Non-parametric 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.
| Original language | English (US) |
|---|---|
| Title of host publication | 2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 588-592 |
| Number of pages | 5 |
| Volume | 2018-January |
| ISBN (Electronic) | 9781509059904 |
| DOIs | |
| State | Published - Mar 7 2018 |
| Event | 5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Montreal, Canada Duration: Nov 14 2017 → Nov 16 2017 |
Publication series
| Name | 2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings |
|---|---|
| Volume | 2018-January |
Other
| Other | 5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 |
|---|---|
| Country/Territory | Canada |
| City | Montreal |
| Period | 11/14/17 → 11/16/17 |
Bibliographical note
Funding Information:The work in this paper was supported by grants NSF 1514056 and ARO W911NF-15-1-0492
Funding Information:
†The work in this paper was supported by grants NSF 1514056 and ARO W911NF-15-1-0492
Keywords
- graph kernel
- graph signal processing
- semi-parametric inference