Semi-parametric graph kernel-based reconstruction

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations
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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 languageEnglish (US)
Title of host publication2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages588-592
Number of pages5
Volume2018-January
ISBN (Electronic)9781509059904
DOIs
StatePublished - Mar 7 2018
Event5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Montreal, Canada
Duration: Nov 14 2017Nov 16 2017

Publication series

Name2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings
Volume2018-January

Other

Other5th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017
CountryCanada
CityMontreal
Period11/14/1711/16/17

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

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