2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSPTM-10.5
Paper Title KERNEL REGRESSION ON GRAPHS IN RANDOM FOURIER FEATURES SPACE
Authors Vitor Elias, Federal University of Rio de Janeiro, Brazil; Vinay Gogineni, Simula Research Laboratory, Norway; Wallace Martins, University of Luxembourg, Luxembourg; Stefan Werner, Norwegian University of Science and Technology, Norway
SessionSPTM-10: Distributed Learning over Graphs
LocationGather.Town
Session Time:Wednesday, 09 June, 14:00 - 14:45
Presentation Time:Wednesday, 09 June, 14:00 - 14:45
Presentation Poster
Topic Signal Processing Theory and Methods: [SIPG] Signal and Information Processing over Graphs
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract 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.