论文标题
用于在空间分布的网络上逆过滤的预处理梯度下降算法
Preconditioned Gradient Descent Algorithm for Inverse Filtering on Spatially Distributed Networks
论文作者
论文摘要
图形过滤器及其对流已被广泛用于降解，平滑，采样，插值和学习。在空间分布式网络（SDN）上实施反向过滤过程是一个非凡的挑战，因为SDN上的每个代理都配备了具有有限容量的数据处理子系统，并且由于工程限制而具有密封范围的通信子系统。在这封信中，我们引入了一种预处理的梯度下降算法，以实现与具有较小的地理宽度的图形滤波器相关的反过滤过程。所提出的算法呈指数收敛，并且可以在顶点级别实现，并应用于SDNS上的时变逆过滤。
Graph filters and their inverses have been widely used in denoising, smoothing, sampling, interpolating and learning. Implementation of an inverse filtering procedure on spatially distributed networks (SDNs) is a remarkable challenge, as each agent on an SDN is equipped with a data processing subsystem with limited capacity and a communication subsystem with confined range due to engineering limitations. In this letter, we introduce a preconditioned gradient descent algorithm to implement the inverse filtering procedure associated with a graph filter having small geodesic-width. The proposed algorithm converges exponentially, and it can be implemented at vertex level and applied to time-varying inverse filtering on SDNs.
