Spectrum-blind signal recovery on graphs

Rohan Varma, Siheng Chen, Jelena Kovacevic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We consider the problem of recovering a graph signal, sparse in the graph spectral domain from a few number of samples. In contrast to most previous work on the sampling of graph signals, the setting is spectrum-blind where we are unaware of the graph d support of the signal. We propose a class of spectrum-blind graph signals and study two recovery strategies based on random and experimentally designed sampling inspired by the compressed sensing paradigm. We further show sampling bounds for graphs, including Erdos-Rényi random graphs. We show that experimentally designed sampling significantly outperforms random sampling for some irregular graph families.

Original languageEnglish (US)
Title of host publication2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages81-84
Number of pages4
ISBN (Electronic)9781479919635
DOIs
StatePublished - Jan 1 2015
Event6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015 - Cancun, Mexico
Duration: Dec 13 2015Dec 16 2015

Other

Other6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015
CountryMexico
CityCancun
Period12/13/1512/16/15

Fingerprint

Recovery
Sampling
Graph in graph theory
Compressed sensing
Compressed Sensing
Random Sampling
Erdös
Random Graphs
Irregular
Paradigm

Keywords

  • compressed sensing
  • discrete signal processing on graphs
  • sampling
  • signal recovery

ASJC Scopus subject areas

  • Signal Processing
  • Computational Mathematics

Cite this

Varma, R., Chen, S., & Kovacevic, J. (2015). Spectrum-blind signal recovery on graphs. In 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015 (pp. 81-84). [7383741] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CAMSAP.2015.7383741

Spectrum-blind signal recovery on graphs. / Varma, Rohan; Chen, Siheng; Kovacevic, Jelena.

2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 81-84 7383741.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Varma, R, Chen, S & Kovacevic, J 2015, Spectrum-blind signal recovery on graphs. in 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015., 7383741, Institute of Electrical and Electronics Engineers Inc., pp. 81-84, 6th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015, Cancun, Mexico, 12/13/15. https://doi.org/10.1109/CAMSAP.2015.7383741
Varma R, Chen S, Kovacevic J. Spectrum-blind signal recovery on graphs. In 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 81-84. 7383741 https://doi.org/10.1109/CAMSAP.2015.7383741
Varma, Rohan ; Chen, Siheng ; Kovacevic, Jelena. / Spectrum-blind signal recovery on graphs. 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 81-84
@inproceedings{184a425a6ee648c09264d5fef59516f9,
title = "Spectrum-blind signal recovery on graphs",
abstract = "We consider the problem of recovering a graph signal, sparse in the graph spectral domain from a few number of samples. In contrast to most previous work on the sampling of graph signals, the setting is spectrum-blind where we are unaware of the graph d support of the signal. We propose a class of spectrum-blind graph signals and study two recovery strategies based on random and experimentally designed sampling inspired by the compressed sensing paradigm. We further show sampling bounds for graphs, including Erdos-R{\'e}nyi random graphs. We show that experimentally designed sampling significantly outperforms random sampling for some irregular graph families.",
keywords = "compressed sensing, discrete signal processing on graphs, sampling, signal recovery",
author = "Rohan Varma and Siheng Chen and Jelena Kovacevic",
year = "2015",
month = "1",
day = "1",
doi = "10.1109/CAMSAP.2015.7383741",
language = "English (US)",
pages = "81--84",
booktitle = "2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Spectrum-blind signal recovery on graphs

AU - Varma, Rohan

AU - Chen, Siheng

AU - Kovacevic, Jelena

PY - 2015/1/1

Y1 - 2015/1/1

N2 - We consider the problem of recovering a graph signal, sparse in the graph spectral domain from a few number of samples. In contrast to most previous work on the sampling of graph signals, the setting is spectrum-blind where we are unaware of the graph d support of the signal. We propose a class of spectrum-blind graph signals and study two recovery strategies based on random and experimentally designed sampling inspired by the compressed sensing paradigm. We further show sampling bounds for graphs, including Erdos-Rényi random graphs. We show that experimentally designed sampling significantly outperforms random sampling for some irregular graph families.

AB - We consider the problem of recovering a graph signal, sparse in the graph spectral domain from a few number of samples. In contrast to most previous work on the sampling of graph signals, the setting is spectrum-blind where we are unaware of the graph d support of the signal. We propose a class of spectrum-blind graph signals and study two recovery strategies based on random and experimentally designed sampling inspired by the compressed sensing paradigm. We further show sampling bounds for graphs, including Erdos-Rényi random graphs. We show that experimentally designed sampling significantly outperforms random sampling for some irregular graph families.

KW - compressed sensing

KW - discrete signal processing on graphs

KW - sampling

KW - signal recovery

UR - http://www.scopus.com/inward/record.url?scp=84963851445&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84963851445&partnerID=8YFLogxK

U2 - 10.1109/CAMSAP.2015.7383741

DO - 10.1109/CAMSAP.2015.7383741

M3 - Conference contribution

AN - SCOPUS:84963851445

SP - 81

EP - 84

BT - 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2015

PB - Institute of Electrical and Electronics Engineers Inc.

ER -