FAST MULTISCALE DIFFUSION ON GRAPHS

被引：0

作者：

Marcotte, S. ^{[1
]}

Barbe, A. ^{[2
]}

Gribonval, R. ^{[2
]}

Vayer, T. ^{[2
]}

Sebban, M. ^{[3
]}

Borgnat, P. ^{[4
]}

Goncalves, P. ^{[2
]}

机构：

[1] ENS Rennes, Rennes, France

[2] Univ Lyon, INRIA, ENSL, CNRS,LIP, Lyon, France

[3] Univ Lyon, Lab Hubert Curien, CNRS, UJM St Etienne, Lyon, France

[4] Univ Lyon, Lab Phys, ENSL, CNRS, Lyon, France

来源：

2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2022年

关键词：

Approximate computing; Chebyshev approximation; Computational efficiency; Estimation error;

D O I：

10.1109/ICASSP43922.2022.9746802

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

Diffusing a graph signal at multiple scales requires to compute the action of the exponential of as many versions of the Laplacian matrix. Considering the truncated Chebyshev polynomial approximation of the exponential, we derive a tightened bound on the approximation error, allowing thus for a better estimate of the polynomial degree that reaches a prescribed error. We leverage the properties of these approximations to factorize the computation of the action of the diffusion operator over multiple scales, thus drastically reducing its computational cost.

引用

页码：5627 / 5631

页数：5

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