Signal recovery on graphs: Random versus experimentally designed sampling

We study signal recovery on graphs based on two sampling strategies: random sampling and experimentally designed sampling. We propose a new class of smooth graph signals, called approximately bandlimited. We then propose two recovery strategies based on random sampling and experimentally designed sa...

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Bibliographic Details
Published in2015 International Conference on Sampling Theory and Applications (SampTA) pp. 337 - 341
Main Authors Siheng Chen, Varma, Rohan, Singh, Aarti, Kovacevic, Jelena
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2015
Subjects
Approximation algorithms
Approximation methods
Bandwidth
Fourier transforms
Frequency estimation
Signal processing algorithms
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Summary:We study signal recovery on graphs based on two sampling strategies: random sampling and experimentally designed sampling. We propose a new class of smooth graph signals, called approximately bandlimited. We then propose two recovery strategies based on random sampling and experimentally designed sampling. The proposed recovery strategy based on experimentally designed sampling uses sampling scores, which is similar to the leverage scores used in the matrix approximation. We show that while both strategies are unbiased estimators for the low-frequency components, the convergence rate of experimentally designed sampling is much faster than that of random sampling when a graph is irregular 1 . We validate the proposed recovery strategies on three specific graphs: a ring graph, an Erdös-Rényi graph, and a star graph. The simulation results support the theoretical analysis.
DOI:10.1109/SAMPTA.2015.7148908