Wide-sense stationarity and spectral estimation for generalized graph signal

Wide-sense stationarity and spectral estimation for generalized graph signal

We consider a probabilistic model for graph signal processing (GSP) in a generalized framework where each vertex of a graph is associated with an element from a Hilbert space. We introduce the notion of joint wide-sense stationarity in this generalized GSP (GGSP) framework, which allows us to charac...

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Main Authors: Jian, Xingchao, Tay, Wee Peng
Other Authors: School of Electrical and Electronic Engineering
Format: Conference or Workshop Item
Language:English
Published: 2022
Subjects:
Engineering::Electrical and electronic engineering
Graph Signal Processing
Hilbert Space
Online Access:https://hdl.handle.net/10356/161587
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1615872022-09-12T02:50:14Z Wide-sense stationarity and spectral estimation for generalized graph signal Jian, Xingchao Tay, Wee Peng School of Electrical and Electronic Engineering ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Centre for Infocomm Technology (INFINITUS) Engineering::Electrical and electronic engineering Graph Signal Processing Hilbert Space We consider a probabilistic model for graph signal processing (GSP) in a generalized framework where each vertex of a graph is associated with an element from a Hilbert space. We introduce the notion of joint wide-sense stationarity in this generalized GSP (GGSP) framework, which allows us to characterize a random graph process as a combination of uncorrelated oscillation modes across both the vertex and Hilbert space domains. We also propose a method for joint power spectral density estimation in case of missing features. Experiment results corroborate the effectiveness of our estimation approach. Ministry of Education (MOE) Submitted/Accepted version This research is supported by the Singapore Ministry of Education Academic Research Fund Tier 2 grant MOE-T2EP20220-0002. 2022-09-12T02:50:14Z 2022-09-12T02:50:14Z 2022 Conference Paper Jian, X. & Tay, W. P. (2022). Wide-sense stationarity and spectral estimation for generalized graph signal. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 5827-5831. https://dx.doi.org/10.1109/ICASSP43922.2022.9747273 9781665405409 https://hdl.handle.net/10356/161587 10.1109/ICASSP43922.2022.9747273 2-s2.0-85131249219 5827 5831 en MOE-T2EP20220-0002 © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/ICASSP43922.2022.9747273. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Graph Signal Processing
Hilbert Space
spellingShingle Engineering::Electrical and electronic engineering
Graph Signal Processing
Hilbert Space
Jian, Xingchao
Tay, Wee Peng
Wide-sense stationarity and spectral estimation for generalized graph signal
description We consider a probabilistic model for graph signal processing (GSP) in a generalized framework where each vertex of a graph is associated with an element from a Hilbert space. We introduce the notion of joint wide-sense stationarity in this generalized GSP (GGSP) framework, which allows us to characterize a random graph process as a combination of uncorrelated oscillation modes across both the vertex and Hilbert space domains. We also propose a method for joint power spectral density estimation in case of missing features. Experiment results corroborate the effectiveness of our estimation approach.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Jian, Xingchao
Tay, Wee Peng
format Conference or Workshop Item
author Jian, Xingchao
Tay, Wee Peng
author_sort Jian, Xingchao
title Wide-sense stationarity and spectral estimation for generalized graph signal
title_short Wide-sense stationarity and spectral estimation for generalized graph signal
title_full Wide-sense stationarity and spectral estimation for generalized graph signal
title_fullStr Wide-sense stationarity and spectral estimation for generalized graph signal
title_full_unstemmed Wide-sense stationarity and spectral estimation for generalized graph signal
title_sort wide-sense stationarity and spectral estimation for generalized graph signal
publishDate 2022
url https://hdl.handle.net/10356/161587
_version_ 1744365422067580928

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