On the Impact of Sample Size in Reconstructing Graph Signals

Abstract

Reconstructing a signal on a graph from observations on a subset of the vertices is a fundamental problem in the field of graph signal processing. It is often assumed that adding additional observations to an observation set will reduce the expected reconstruction error. We show that under the setting of noisy observation and least-squares reconstruction this is not always the case, characterising the behaviour both theoretically and experimentally.

Sampling Theory and Applications (SampTA) 2023, Jul 2023, New Haven (Yale University), United States