Type
Conference PaperKAUST Grant Number
OSR-2015-Sensors-2700Date
2016-10-06Online Publication Date
2016-10-06Print Publication Date
2016-07Permanent link to this record
http://hdl.handle.net/10754/623600
Metadata
Show full item recordAbstract
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.Citation
Chepuri 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.Sponsors
This work was supported by the KAUST-MIT-TUD consortium grant OSR-2015-Sensors-2700.Conference/Event name
2016 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2016ae974a485f413a2113503eed53cd6c53
10.1109/sam.2016.7569707