Space-shift sampling of graph signals

Santiago Segarra, Antonio G. Marques, Geert Leus, Alejandro Ribeiro

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

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Abstract

A novel scheme for sampling graph signals is proposed. Space-shift sampling can be understood as a hybrid scheme that combines selection sampling -- observing the signal values on a subset of nodes - and aggregation sampling - observing the signal values at a single node after successive aggregation of local data. Under the assumption of bandlimitedness, we state conditions and propose strategies for signal recovery in different settings. Being a more general procedure, space-shift sampling achieves smaller reconstruction errors than current schemes, as we illustrate through the reconstruction of the industrial activity in a graph of the U.S. economy.
Original languageEnglish
Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Subtitle of host publicationProceedings
EditorsMin Dong, Thomas Fang Zheng
Place of PublicationDanvers, MA
PublisherIEEE
Pages6355-6359
Number of pages5
ISBN (Electronic)978-1-4799-9988-0
DOIs
Publication statusPublished - 19 May 2016
Event2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai International Convention Center, Shanghai, China
Duration: 20 Mar 201625 Mar 2016

Conference

Conference2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
Abbreviated titleICASSP
CountryChina
CityShanghai
Period20/03/1625/03/16

Keywords

  • Reconstruction
  • Graph signal processing
  • Space-shift sampling
  • Bandlimited signal

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