Active Sampling for Approximately Bandlimited Graph Signals

TL;DR

This paper introduces an active sampling method for estimating approximately bandlimited graph signals, using an EM-based approach to adaptively select samples and improve estimation efficiency.

Contribution

It proposes a novel active sampling algorithm that accounts for unknown parameters and iteratively refines sampling decisions for graph signals.

Findings

Reduces sample size needed for accurate estimation.

Outperforms existing methods in simulations.

Effectively handles unknown model parameters.

Abstract

This paper investigates the active sampling for estimation of approximately bandlimited graph signals. With the assistance of a graph filter, an approximately bandlimited graph signal can be formulated by a Gaussian random field over the graph. In contrast to offline sampling set design methods which usually rely on accurate prior knowledge about the model, unknown parameters in signal and noise distribution are allowed in the proposed active sampling algorithm. The active sampling process is divided into two alternating stages: unknown parameters are first estimated by Expectation Maximization (EM), with which the next node to sample is selected based on historical observations according to predictive uncertainty. Validated by simulations compared with related approaches, the proposed algorithm can reduce the sample size to reach a certain estimation accuracy.