KAUST Grant NumberOSR-2015-Sensors-2700
Online Publication Date2016-10-06
Print Publication Date2016-07
Permanent link to this recordhttp://hdl.handle.net/10754/623600
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AbstractIn 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.
CitationChepuri SP, Leus G (2016) Subsampling for graph power spectrum estimation. 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM). Available: http://dx.doi.org/10.1109/sam.2016.7569707.
SponsorsThis work was supported by the KAUST-MIT-TUD consortium grant OSR-2015-Sensors-2700.
Conference/Event name2016 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2016