Active Sampling for Approximately Bandlimited Graph Signals

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.

arXiv commentICASSP 2019

References in corpus (7)

• The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains
• Discrete Signal Processing on Graphs: Sampling Theory
• Random Geometric Graphs
• Efficient Sampling Set Selection for Bandlimited Graph Signals Using Graph Spectral Proxies
• Sampling of graph signals with successive local aggregations
• GSPBOX: A toolbox for signal processing on graphs
• Chebyshev Polynomial Approximation for Distributed Signal Processing