TY - GEN
T1 - Subsampling for graph power spectrum estimation
AU - Chepuri, Sundeep Prabhakar
AU - Leus, Geert
N1 - KAUST 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.
PY - 2016/10/6
Y1 - 2016/10/6
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/623600
UR - http://ieeexplore.ieee.org/document/7569707/
UR - http://www.scopus.com/inward/record.url?scp=84990847918&partnerID=8YFLogxK
U2 - 10.1109/sam.2016.7569707
DO - 10.1109/sam.2016.7569707
M3 - Conference contribution
SN - 9781509021031
BT - 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM)
PB - Institute of Electrical and Electronics Engineers (IEEE)
ER -