Semi-parametric graph kernel-based reconstruction

Signal reconstruction over graphs arises naturally in diverse science and engineering applications. Existing methods employ either parametric or nonparametric approaches based on graph kernels. Although the former are adequate when the signals of interest adhere to postulated models, their performan...

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Bibliographic Details
Published in2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP) pp. 588 - 592
Main Authors Ioannidis, Vassilis N., Nikolakopoulos, Athanasios N., Giannakis, Georgios B.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2017
Subjects
graph kernel
graph signal processing
Kernel
Laplace equations
Optimization
semi-parametric inference
Signal reconstruction
Signal to noise ratio
Task analysis
Topology
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Summary:Signal reconstruction over graphs arises naturally in diverse science and engineering applications. Existing methods employ either parametric or nonparametric approaches based on graph kernels. Although the former are adequate when the signals of interest adhere to postulated models, their performance degrades rapidly under model mismatch. Non-parametric alternatives on the other hand are flexible, but not as parsimonious in capturing prior information. Targeting a hybrid "sweet spot," the present contribution advocates an efficient semi-parametric approach capable of incorporating known signal structure without sacrificing the flexibility of the overall model. Numerical tests on synthetic as well as real data corroborate that the novel method leads to markedly improved signal reconstruction performance.
DOI:10.1109/GlobalSIP.2017.8309027