Sparse-graph-learning

"Graph Learning under sparsity priors" addresses the issue of inferring the structure of the data from the mere graph signal observations. It is assumed that the graph signals can be represented as a sparse linear combination of a few components of a structured graph dictionary. The dictionary is constructed on polynomials of the graph Laplacian, which can sparsely represent a general class of graph signals.