Supervised Linear Regression for Graph Learning from Graph Signals
Information Theory
2018-11-06 v1 math.IT
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.
Cite
@article{arxiv.1811.01586,
title = {Supervised Linear Regression for Graph Learning from Graph Signals},
author = {Arun Venkitaraman and Hermina Petric Maretic and Saikat Chatterjee and Pascal Frossard},
journal= {arXiv preprint arXiv:1811.01586},
year = {2018}
}