Subsampling for graph power spectrum estimation
Subsampling for graph power spectrum estimation
In this paper we focus on subsampling stationary random processes 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.
Contains 4 figures. Matlab scripts to reproduce these results can be downloaded from: http://cas.et.tudelft.nl/~sundeep/sw/gpsd.zip
- Delft University of Technology Netherlands
Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Information Theory, Information Theory (cs.IT), FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Signal Processing
Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Information Theory, Information Theory (cs.IT), FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Signal Processing
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