Approximate fast graph Fourier transforms via multi-layer sparse approximations
For researchers in graph signal processing, this work addresses the lack of fast graph Fourier transforms, but the results are preliminary and incremental.
The paper tackles the problem of computing graph Fourier transforms efficiently, proposing a method based on greedy approximate diagonalization of the graph Laplacian that achieves fast application and efficient storage. Experiments on synthetic and real graphs demonstrate its potential, though no concrete speedup numbers are provided.
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.