Supervised Linear Regression for Graph Learning from Graph Signals

Abstract

We propose a supervised learning approach for predicting an underlying graph from a set of graph signals. Our approach is based on linear regression. In the linear regression model, we predict edge-weights of a graph as the output, given a set of signal values on nodes of the graph as the input. We solve for the optimal regression coefficients using a relevant optimization problem that is convex and uses a graph-Laplacian based regularization. The regularization helps to promote a specific graph spectral profile of the graph signals. Simulation experiments demonstrate that our approach predicts well even in presence of outliers in input data.

References in corpus (4)

• The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains
• Learning graphs from data: A signal representation perspective
• Kernel-based Reconstruction of Graph Signals
• Signal Processing on Graphs: Causal Modeling of Unstructured Data

Cited by in corpus (1)

• Recursive Prediction of Graph Signals with Incoming Nodes