Generalized Graph Spectral Sampling with Stochastic Priors

We consider generalized sampling for stochastic graph signals. The generalized graph sampling framework allows recovery of graph signals beyond the bandlimited setting by placing a correction filter between the sampling and reconstruction operators and assuming an appropriate prior. In this paper, we assume the graph signals are modeled by graph wide sense stationarity (GWSS), which is an extension of WSS for standard time domain signals. Furthermore, sampling is performed in the graph frequency domain along with the assumption that the graph signals lie in a periodic graph spectrum subspace. The correction filter is designed by minimizing the mean-squared error (MSE). The graph spectral response of the correction filter parallels that in generalized sampling for WSS signals. The effectiveness of our approach is validated via experiments by comparing the MSE with existing approaches.