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Fast Multiscale Diffusion on Graphs

Abstract : Diffusing a graph signal at multiple scales requires computing the action of the exponential of several multiples of the Laplacian matrix. We tighten a bound on the approximation error of truncated Chebyshev polynomial approximations of the exponential, hence significantly improving a priori estimates of the polynomial order for a prescribed error. We further exploit properties of these approximations to factorize the computation of the action of the diffusion operator over multiple scales, thus reducing drastically its computational cost.
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https://hal.archives-ouvertes.fr/hal-03212764
Contributor : Rémi Gribonval Connect in order to contact the contributor
Submitted on : Wednesday, February 16, 2022 - 1:48:26 PM
Last modification on : Thursday, November 17, 2022 - 4:06:40 AM

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Sibylle Marcotte, Amélie Barbe, Rémi Gribonval, Titouan Vayer, Marc Sebban, et al.. Fast Multiscale Diffusion on Graphs. ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing, May 2022, Singapore, Singapore. ⟨10.1109/ICASSP43922.2022.9746802⟩. ⟨hal-03212764v2⟩

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