Reference : Kernel Regression on Graphs in Random Fourier Features Space
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Electrical & electronics engineering
Kernel Regression on Graphs in Random Fourier Features Space
Elias, Vitor R. M. mailto []
Gogenini, Vinay C. mailto []
Alves Martins, Wallace mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom >]
Werner, Stefan mailto []
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-2021), Toronto 6-11 June 2021
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-2021)
from 06-06-2021 to 11-06-2021
[en] kernel regression on graphs ; online learning on graphs ; random Fourier features ; stochastic gradient
[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.
H2020 ; 742648 - AGNOSTIC - Actively Enhanced Cognition based Framework for Design of Complex Systems

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