Distributed Diffusion Adaptation over Graph Signals

Roula Nassif, Cedric Richard, Jie Chen, Ali H. Sayed

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

42 Scopus citations

Abstract

Most works on graph signal processing assume static graph signals, which is a limitation even in comparison to traditional DSP techniques where signals are modeled as sequences that evolve over time. For broader applicability, it is necessary to develop techniques that are able to process dynamic or streaming data. Many earlier works on adaptive networks have addressed problems related to this challenge by developing effective strategies that are particularly well-suited to data streaming into graphs. We are thus faced with two paradigms: one where signals are modeled as static and sitting on the graph nodes, and another where signals are modeled as dynamic and streaming into the graph nodes. The objective of this work is to blend these concepts and propose diffusion strategies for adaptively learning from streaming graph signals.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4129-4133
Number of pages5
ISBN (Print)9781538646588
DOIs
StatePublished - 10 Sep 2018
Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada
Duration: 15 Apr 201820 Apr 2018

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2018-April
ISSN (Print)1520-6149

Conference

Conference2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Country/TerritoryCanada
CityCalgary
Period15/04/1820/04/18

Keywords

  • Diffusion strategies
  • Graph filtering
  • Graph signal processing
  • Streaming graph signals

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