Polynomial Graphical Lasso: Learning Edges from Gaussian Graph-Stationary Signals

arXiv:2404.02621v1 Announce Type: cross
Abstract: This paper introduces Polynomial Graphical Lasso (PGL), a new approach to learning graph structures from nodal signals. Our key contribution lies in modeling the signals as Gaussian and stationary on the graph, enabling the development of a graph-learning formulation that combines the strengths of graphical lasso with a more encompassing model. Specifically, we assume that the precision matrix can take any polynomial form of the sought graph, allowing for increased flexibility in modeling nodal relationships. …

abstract arxiv cs.lg development eess.sp enabling graph key lasso lies modeling paper polynomial the graph type