In this paper we focus on subsampling stationary random signals that reside on the vertices of undirected graphs. Second-order stationary graph signals are obtained by filtering white noise and they admit a well-defined power spectrum. Estimating the graph power spectrum forms a central component of stationary graph signal processing and related inference tasks. We show that by sampling a significantly smaller subset of vertices and using simple least squares, we can reconstruct the power spectrum of the graph signal from the subsampled observations, without any spectral priors. In addition, a near-optimal greedy algorithm is developed to design the subsampling scheme.
|Title of host publication
|2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)
|Institute of Electrical and Electronics Engineers (IEEE)
|Published - Oct 6 2016
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): OSR-2015-Sensors-2700
Acknowledgements: This work was supported by the KAUST-MIT-TUD consortium grant OSR-2015-Sensors-2700.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.