TY - GEN
T1 - Generalized Graph Spectral Sampling with Stochastic Priors
AU - Hara, Junya
AU - Tanaka, Yuichi
AU - Eldar, Yonina C.
N1 - Publisher Copyright: © 2020 IEEE.
PY - 2020/5/14
Y1 - 2020/5/14
N2 - 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.
AB - 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.
KW - Graph signal processing
KW - Wiener filter
KW - minimal reconstruction error
KW - spectral domain sampling
UR - https://www.scopus.com/pages/publications/85089232934
U2 - 10.1109/ICASSP40776.2020.9053720
DO - 10.1109/ICASSP40776.2020.9053720
M3 - Conference contribution
SN - 9781509066315
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 5680
EP - 5684
BT - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
T2 - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Y2 - 4 May 2020 through 8 May 2020
ER -