The network nullspace property for compressed sensing of big data over networks

Madelon Hulsebos, Alexander Jung

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

We present a novel condition, which we term the network nullspace property, which ensures accurate recovery of graph signals representing massive network-structured datasets from few signal values. The network nullspace property couples the cluster structure of the underlying network-structure with the geometry of the sampling set. Our results can be used to design efficient sampling strategies based on the network topology.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PublisherIEEE
Pages4549-4553
Number of pages5
Volume2018-April
ISBN (Print)9781538646588
DOIs
Publication statusPublished - 10 Sep 2018
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Acoustics, Speech, and Signal Processing - Calgary, Canada
Duration: 15 Apr 201820 Apr 2018
https://2018.ieeeicassp.org/

Publication series

NameProceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
PublisherIEEE
ISSN (Electronic)2379-190X

Conference

ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated titleICASSP
Country/TerritoryCanada
CityCalgary
Period15/04/201820/04/2018
Internet address

Keywords

  • Big data
  • Complex networks
  • Compressed sensing
  • Convex optimization
  • Semi-supervised learning

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