  We introduce a set of novel multiscale basis transforms for signals on graphs
that utilize their "dual" domains by incorporating the "natural" distances
between graph Laplacian eigenvectors, rather than simply using the eigenvalue
ordering. These basis dictionaries can be seen as generalizations of the
classical Shannon wavelet packet dictionary to arbitrary graphs, and do not
rely on the frequency interpretation of Laplacian eigenvalues. We describe the
algorithms (involving either vector rotations or orthogonalizations) to
construct these basis dictionaries, use them to efficiently approximate graph
signals through the best basis search, and demonstrate the strengths of these
basis dictionaries for graph signals measured on sunflower graphs and street
networks.
