Graph Structured Dictionary for Regression

Transformations for signals defined on graphs are playing significant role in applying the emerging graph signal processing techniques to different tasks. In this paper we focus on utilizing graph signal dictionary, a data-driven transformation, for regression. Apart from spelling out the joint optimization formulation, as well as the associated iteration steps to arrive at the dictionary and the regression coefficients, the paper provides some initial results to bring out the usefulness of the proposed approach.