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Title: Wide-sense stationarity and spectral estimation for generalized graph signal
Authors: Jian, Xingchao
Tay, Wee Peng
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: 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.
Project: MOE-T2EP20220-0002 
metadata.dc.contributor.conference: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Abstract: 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.
ISBN: 9781665405409
DOI: 10.1109/ICASSP43922.2022.9747273
Schools: School of Electrical and Electronic Engineering 
Research Centres: Centre for Infocomm Technology (INFINITUS) 
Rights: © 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:
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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