A-Optimal Sampling and Robust Reconstruction for Graph Signals via Truncated Neumann Series

TL;DR

This paper introduces a novel A-optimal sampling method for graph signals that leverages truncated Neumann series and Chebyshev polynomial approximations to improve reconstruction fidelity and robustness in noisy environments.

Contribution

It proposes a new sampling strategy based on Neumann series and polynomial approximation, enhancing robustness and reducing complexity in graph signal reconstruction.

Findings

Outperforms previous sampling strategies in MSE performance

Uses Chebyshev polynomial to approximate ideal low-pass filter

Provides a robust signal reconstruction method

Abstract

Graph signal processing (GSP) studies signals that live on irregular data kernels described by graphs. One fundamental problem in GSP is sampling---from which subset of graph nodes to collect samples in order to reconstruct a bandlimited graph signal in high fidelity. In this paper, we seek a sampling strategy that minimizes the mean square error (MSE) of the reconstructed bandlimited graph signals assuming an independent and identically distributed (iid) noise model---leading naturally to the A-optimal design criterion. To avoid matrix inversion, we first prove that the inverse of the information matrix in the A-optimal criterion is equivalent to a Neumann matrix series. We then transform the truncated Neumann series based sampling problem into an equivalent expression that replaces eigenvectors of the Laplacian operator with a sub-matrix of an ideal low-pass graph filter. Finally, we approximate the ideal filter using a Chebyshev matrix polynomial. We design a greedy algorithm to iteratively minimize the simplified objective. For signal reconstruction, we propose an accompanied signal reconstruction strategy that reuses the approximated filter sub-matrix and is provably more robust than conventional least square recovery. Simulation results show that our sampling strategy outperforms two previous strategies in MSE performance at comparable complexity.