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arXiv · 1603.03697

Subsampling for Graph Power Spectrum Estimation

Sundeep Prabhakar Chepuri·Geert Leus
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Abstract

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.

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Sundeep Prabhakar Chepuri, Geert Leus. 2016-03-11. Subsampling for Graph Power Spectrum Estimation. https://arxiv.org/abs/1603.03697

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