Scalable graph signal recovery for big data over networks

Alex Jung, Peter Berger, Gabor Hannak, Gerald Matz

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

8 Citations (Scopus)

Abstract

We formulate the recovery of a graph signal from noisy samples taken on a subset of graph nodes as a convex optimization problem that balances the empirical error for explaining the observed values and a complexity term quantifying the smoothness of the graph signal. To solve this optimization problem, we propose to combine the alternating direction method of multipliers with a novel denoising method that minimizes total variation. Our algorithm can be efficiently implemented in a distributed manner using message passing and thus is attractive for big data problems over networks.

Original languageEnglish
Title of host publicationSPAWC 2016 - 17th IEEE International Workshop on Signal Processing Advances in Wireless Communications
PublisherIEEE
Number of pages6
Volume2016-August
ISBN (Electronic)9781509017492
DOIs
Publication statusPublished - 9 Aug 2016
MoE publication typeA4 Article in a conference publication
EventIEEE International Workshop on Signal Processing Advances in Wireless Communications - Edinburgh, United Kingdom
Duration: 3 Jul 20166 Jul 2016
Conference number: 17

Workshop

WorkshopIEEE International Workshop on Signal Processing Advances in Wireless Communications
Abbreviated titleSPAWC
CountryUnited Kingdom
CityEdinburgh
Period03/07/201606/07/2016

Keywords

  • ADMM
  • big data
  • graph signal recovery
  • message passing
  • total variation

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