Near-Optimal Graph Signal Sampling by Pareto Optimization

Dongqi Luo, Binqiang Si, Saite Zhang, Fan Yu, Jihong Zhu
2021 Sensors  
In this paper, we focus on the bandlimited graph signal sampling problem. To sample graph signals, we need to find small-sized subset of nodes with the minimal optimal reconstruction error. We formulate this problem as a subset selection problem, and propose an efficient Pareto Optimization for Graph Signal Sampling (POGSS) algorithm. Since the evaluation of the objective function is very time-consuming, a novel acceleration algorithm is proposed in this paper as well, which accelerates the
more » ... accelerates the evaluation of any solution. Theoretical analysis shows that POGSS finds the desired solution in quadratic time while guaranteeing nearly the best known approximation bound. Empirical studies on both Erdos-Renyi graphs and Gaussian graphs demonstrate that our method outperforms the state-of-the-art greedy algorithms.
doi:10.3390/s21041415 pmid:33670548 fatcat:55yju544xrcm5j74aczrkekoma