Groups
Spectral normalization rescales a weight matrix so its largest singular value (spectral norm) is at most a target value, typically 1.
Spectral convolution on graphs generalizes the classical notion of convolution using the graph’s Laplacian eigenvectors as “Fourier” basis functions.