Towards bandwidth estimation for graph signal reconstruction

TL;DR

This paper introduces a method to estimate the bandwidth of bandlimited graph signals to improve reconstruction accuracy, using cross-validation to predict errors and select optimal bandwidths on real-world graphs.

Contribution

It proposes a novel cross-validation based approach for estimating graph signal bandwidths, enhancing reconstruction stability and accuracy.

Findings

Effective error estimation on real-world graphs

Improved bandwidth selection for signal reconstruction

Enhanced stability in graph signal processing

Abstract

In numerous graph signal processing applications, data is often missing for a variety of reasons, and predicting the missing data is essential. In this paper, we consider data on graphs modeled as bandlimited graph signals. Predicting or reconstructing the unknown signal values for such a model requires an estimate of the signal bandwidth. In this paper, we address the problem of estimating the reconstruction errors, minimizing which would thereby provide an estimate of the signal bandwidth. In doing so, we design a cross-validation approach needed for stable graph signal reconstruction and propose a method for estimating the reconstruction errors for different choices of signal bandwidth. Using this technique, we are able to estimate the reconstruction error on a variety of real-world graphs.