Subsampling for graph power spectrum estimation

Sundeep Prabhakar Chepuri; Geert Leus

doi:10.1109/sam.2016.7569707

Subsampling for graph power spectrum estimation

Sundeep Prabhakar Chepuri, Geert Leus

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

22 Scopus citations

Abstract

In this paper we focus on subsampling stationary random signals that reside on the vertices of undirected graphs. Second-order stationary graph signals are obtained by filtering white noise and they admit a well-defined power spectrum. Estimating the graph power spectrum forms a central component of stationary graph signal processing and related inference tasks. We show that by sampling a significantly smaller subset of vertices and using simple least squares, we can reconstruct the power spectrum of the graph signal from the subsampled observations, without any spectral priors. In addition, a near-optimal greedy algorithm is developed to design the subsampling scheme.

Original language	English (US)
Title of host publication	2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
ISBN (Print)	9781509021031
DOIs	https://doi.org/10.1109/sam.2016.7569707
State	Published - Oct 6 2016
Externally published	Yes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): OSR-2015-Sensors-2700
Acknowledgements: This work was supported by the KAUST-MIT-TUD consortium grant OSR-2015-Sensors-2700.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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10.1109/sam.2016.7569707

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@inproceedings{084ab418dc264bf884ce70bb14a299ec,

title = "Subsampling for graph power spectrum estimation",

abstract = "In this paper we focus on subsampling stationary random signals that reside on the vertices of undirected graphs. Second-order stationary graph signals are obtained by filtering white noise and they admit a well-defined power spectrum. Estimating the graph power spectrum forms a central component of stationary graph signal processing and related inference tasks. We show that by sampling a significantly smaller subset of vertices and using simple least squares, we can reconstruct the power spectrum of the graph signal from the subsampled observations, without any spectral priors. In addition, a near-optimal greedy algorithm is developed to design the subsampling scheme.",

author = "Chepuri, {Sundeep Prabhakar} and Geert Leus",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledged KAUST grant number(s): OSR-2015-Sensors-2700 Acknowledgements: This work was supported by the KAUST-MIT-TUD consortium grant OSR-2015-Sensors-2700. This publication acknowledges KAUST support, but has no KAUST affiliated authors.",

year = "2016",

month = oct,

day = "6",

doi = "10.1109/sam.2016.7569707",

language = "English (US)",

isbn = "9781509021031",

booktitle = "2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

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T1 - Subsampling for graph power spectrum estimation

AU - Chepuri, Sundeep Prabhakar

AU - Leus, Geert

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledged KAUST grant number(s): OSR-2015-Sensors-2700 Acknowledgements: This work was supported by the KAUST-MIT-TUD consortium grant OSR-2015-Sensors-2700. This publication acknowledges KAUST support, but has no KAUST affiliated authors.

PY - 2016/10/6

Y1 - 2016/10/6

N2 - In this paper we focus on subsampling stationary random signals that reside on the vertices of undirected graphs. Second-order stationary graph signals are obtained by filtering white noise and they admit a well-defined power spectrum. Estimating the graph power spectrum forms a central component of stationary graph signal processing and related inference tasks. We show that by sampling a significantly smaller subset of vertices and using simple least squares, we can reconstruct the power spectrum of the graph signal from the subsampled observations, without any spectral priors. In addition, a near-optimal greedy algorithm is developed to design the subsampling scheme.

AB - In this paper we focus on subsampling stationary random signals that reside on the vertices of undirected graphs. Second-order stationary graph signals are obtained by filtering white noise and they admit a well-defined power spectrum. Estimating the graph power spectrum forms a central component of stationary graph signal processing and related inference tasks. We show that by sampling a significantly smaller subset of vertices and using simple least squares, we can reconstruct the power spectrum of the graph signal from the subsampled observations, without any spectral priors. In addition, a near-optimal greedy algorithm is developed to design the subsampling scheme.

UR - http://hdl.handle.net/10754/623600

UR - http://ieeexplore.ieee.org/document/7569707/

UR - http://www.scopus.com/inward/record.url?scp=84990847918&partnerID=8YFLogxK

U2 - 10.1109/sam.2016.7569707

DO - 10.1109/sam.2016.7569707

M3 - Conference contribution

SN - 9781509021031

BT - 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -

Subsampling for graph power spectrum estimation

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