Handle URI:
http://hdl.handle.net/10754/623600
Title:
Subsampling for graph power spectrum estimation
Authors:
Chepuri, Sundeep Prabhakar; Leus, Geert
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.
Citation:
Chepuri SP, Leus G (2016) Subsampling for graph power spectrum estimation. 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM). Available: http://dx.doi.org/10.1109/sam.2016.7569707.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)
KAUST Grant Number:
OSR-2015-Sensors-2700
Conference/Event name:
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2016
Issue Date:
6-Oct-2016
DOI:
10.1109/sam.2016.7569707
Type:
Conference Paper
Sponsors:
This work was supported by the KAUST-MIT-TUD consortium grant OSR-2015-Sensors-2700.
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorChepuri, Sundeep Prabhakaren
dc.contributor.authorLeus, Geerten
dc.date.accessioned2017-05-15T10:35:10Z-
dc.date.available2017-05-15T10:35:10Z-
dc.date.issued2016-10-06en
dc.identifier.citationChepuri SP, Leus G (2016) Subsampling for graph power spectrum estimation. 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM). Available: http://dx.doi.org/10.1109/sam.2016.7569707.en
dc.identifier.doi10.1109/sam.2016.7569707en
dc.identifier.urihttp://hdl.handle.net/10754/623600-
dc.description.abstractIn 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.en
dc.description.sponsorshipThis work was supported by the KAUST-MIT-TUD consortium grant OSR-2015-Sensors-2700.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.subjectpower spectrum estimationen
dc.subjectGraph signal processingen
dc.subjectstationary graph signalsen
dc.subjectcovariance samplingen
dc.subjectsubsamplingen
dc.titleSubsampling for graph power spectrum estimationen
dc.typeConference Paperen
dc.identifier.journal2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)en
dc.conference.date2016-07-10 to 2016-07-13en
dc.conference.name2016 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2016en
dc.conference.locationRio de Rio de Janeiro, BRAen
dc.contributor.institutionDelft University of Technology (TU Delft), The Netherlandsen
kaust.grant.numberOSR-2015-Sensors-2700en
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