Graph topology inference based on sparsifying transform learning
Graph-based representations play a key role in machine learning. The fundamental step in these representations is the association of a graph structure to a dataset. In this paper, we propose a method that finds a block sparse representation of the data by associating a graph, whose Laplacian matrix admits the sparsifying dictionary as its eigenvectors. The main idea is to associate a graph topology to the data in order to make the observed signals band-limited over the inferred graph.