Graph signal recovery from incomplete and noisy information using approximate message passing

Gita Babazadeh Eslamlou, Alex Jung, Norbert Goertz, Mehdi Fereydooni

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

5 Citations (Scopus)

Abstract

We consider the problem of recovering a graph signal from noisy and incomplete information. In particular, we propose an approximate message passing based iterative method for graph signal recovery. The recovery of the graph signal is based on noisy signal values at a small number of randomly selected nodes. Our approach exploits the smoothness of typical graph signals occurring in many applications, such as wireless sensor networks or social network analysis. The graph signals are smooth in the sense that neighboring nodes have similar signal values. Methodologically, our algorithm is a new instance of the denoising based approximate message passing framework introduced recently by Metzler et. al. We validate the performance of the proposed recovery method via numerical experiments. In certain scenarios our algorithm outperforms existing methods.

Original languageEnglish
Title of host publicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
PublisherIEEE
Pages6170-6174
Number of pages5
Volume2016-May
ISBN (Print)9781479999880
DOIs
Publication statusPublished - 18 May 2016
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Acoustics, Speech, and Signal Processing - Shanghai, China
Duration: 20 Mar 201625 Mar 2016
Conference number: 41
http://www.icassp2016.org/

Conference

ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated titleICASSP 2016
CountryChina
CityShanghai
Period20/03/201625/03/2016
Internet address

Keywords

  • approximate message passing
  • compressed sensing
  • Graph signal denoising
  • subsampling

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