Random Sampling of Bandlimited Graph Signals From Local Measurements

Sampling on graph signals is one of the fundamental topics in graph signal processing. In this letter, we consider the random sampling of
$k$
-bandlimited signals from the local measurements and show that no more than
$O(k\log k)$
measurements with replacement are sufficient for the accurate and stable recovery of any
$k$
-bandlimited graph signals. We propose two random sampling strategies based on the minimum measurements, i.e., the optimal sampling and the estimated sampling. The geodesic distance between vertices is introduced to design the sampling probability distribution. Numerical experiments are included to show the effectiveness of the proposed methods.