Graph Local-Smooth Dictionary Learning

Abstract

In this work, we leverage recent graph uncertainty principles to introduce a new dictionary learning method on graphs. Our method considers two distinct classes of atoms, spatially local atoms on the graph, and smooth atoms, together with well suited penalty functions. Notably, the consideration of the notion of localized atoms on graphs allows to model local and interpretable phenomena.