kernel regression on graphs; online learning on graphs; random Fourier features; stochastic gradient
Résumé :
[en] This work proposes an efficient batch-based implementation for kernel regression on graphs (KRG) using random Fourier features (RFF) and a low-complexity online implementation. Kernel regression has proven to be an efficient learning tool in the graph signal processing framework. However, it suffers from poor scalability inherent to kernel methods. We employ RFF to overcome this issue and derive a batch-based KRG whose model size is independent of the training sample size. We then combine it with a stochastic gradient-descent approach to propose an online algorithm for KRG, namely the stochastic-gradient KRG (SGKRG). We also derive sufficient conditions for convergence in the mean sense of the online algorithms. We validate the performance of the proposed algorithms through numerical experiments using both synthesized and real data. Results show that the proposed batch-based implementation can match the performance of conventional KRG while having reduced complexity. Moreover, the online implementations effectively learn the target model and achieve competitive performance compared to the batch implementations.
Disciplines :
Ingénierie électrique & électronique
Auteur, co-auteur :
Elias, Vitor R. M.
Gogenini, Vinay C.
ALVES MARTINS, Wallace ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
Werner, Stefan
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Kernel Regression on Graphs in Random Fourier Features Space
Date de publication/diffusion :
2021
Nom de la manifestation :
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-2021)
Date de la manifestation :
from 06-06-2021 to 11-06-2021
Sur invitation :
Oui
Manifestation à portée :
International
Titre de l'ouvrage principal :
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-2021), Toronto 6-11 June 2021
Peer reviewed :
Peer reviewed
Projet européen :
H2020 - 742648 - AGNOSTIC - Actively Enhanced Cognition based Framework for Design of Complex Systems
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