Approximate fast graph Fourier transforms via multi-layer sparse approximations

TL;DR

This paper introduces a fast, approximate method for computing graph Fourier transforms using multi-layer sparse approximations, enabling rapid application and efficient storage for arbitrary graphs.

Contribution

It proposes a novel greedy diagonalization approach based on a modified Jacobi algorithm for fast approximate graph Fourier transforms.

Findings

Effective on synthetic and real graphs

Achieves rapid computation with approximate diagonalization

Potential for scalable graph signal processing

Abstract

The Fast Fourier Transform (FFT) is an algorithm of paramount importance in signal processing as it allows to apply the Fourier transform in O(n log n) instead of O(n 2) arithmetic operations. Graph Signal Processing (GSP) is a recent research domain that generalizes classical signal processing tools, such as the Fourier transform, to situations where the signal domain is given by any arbitrary graph instead of a regular grid. Today, there is no method to rapidly apply graph Fourier transforms. We propose in this paper a method to obtain approximate graph Fourier transforms that can be applied rapidly and stored efficiently. It is based on a greedy approximate diagonalization of the graph Laplacian matrix, carried out using a modified version of the famous Jacobi eigenvalues algorithm. The method is described and analyzed in detail, and then applied to both synthetic and real graphs, showing its potential.