Graph Dictionary Learning

Yael Yankelevsky’s work on handling graph-based signals has been reported in several papers recent papers (see the journal publications list). The core idea has been to take into account the Laplacian matrix of the graph signals, and extend the dictionary leanring to accomodate for it. Indeed, our work had additional ideas incorporated within this scheme: (1) we learn the Laplacian matrix within the dictionary learning process; (ii) we take into account another Laplacian – one that accounts for interrelations between the given signals, thus turning our pursuit into a joint one; and (iii) we can handle high dimensional graphs by introducing double-sparsity and a graph-wavelet transform. Two accompanying packages are provided to reproduce all the results shown in these papers: