Sampling Graph Signals with Sparse Dictionary Representation

Kaiwen Zhang, Mario Coutino, Elvin Isufi

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)
131 Downloads (Pure)

Abstract

Graph sampling strategies require the signal to be relatively sparse in an alternative domain, e.g. bandlimitedness for reconstructing the signal. When such a condition is violated or its approximation demands a large bandwidth, the reconstruction often comes with unsatisfactory results even with large samples. In this paper, we propose an alternative sampling strategy based on a type of overcomplete graph-based dictionary. The dictionary is built from graph filters and has demonstrated excellent sparse representations for graph signals. We recognize the proposed sampling problem as a coupling between support recovery of sparse signals and node selection. Thus, to approach the problem we propose a sampling procedure that alternates between these two. The former estimates the sparse support via orthogonal matching pursuit (OMP), which in turn enables the latter to build the sampling set selection through greedy algorithms. Numerical results corroborate the role of key parameters and the effectiveness of the proposed method.
Original languageEnglish
Title of host publication2021 29th European Signal Processing Conference (EUSIPCO)
Subtitle of host publicationProceedings
PublisherIEEE
Pages1815-1819
Number of pages5
ISBN (Electronic)978-9-0827-9706-0
ISBN (Print)978-1-6654-0900-1
DOIs
Publication statusPublished - 2021
Event2021 29th European Signal Processing Conference (EUSIPCO) - Virtual at Dublin, Ireland
Duration: 23 Aug 202127 Aug 2021
Conference number: 29th

Conference

Conference2021 29th European Signal Processing Conference (EUSIPCO)
Abbreviated titleEUSIPCO 2021
Country/TerritoryIreland
CityVirtual at Dublin
Period23/08/2127/08/21

Bibliographical note

Accepted author manuscript

Keywords

  • Compressive sensing
  • Graph signal processing
  • Graph signal sampling
  • Signal reconstruction
  • Sparse sensing

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