Supervised Linear Regression for Graph Learning from Graph Signals

Authors: Arun Venkitaraman, Hermina Petric Maretic, Saikat Chatterjee, Pascal Frossard

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.

Submitted to arXiv on 05 Nov. 2018

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