桂林电子科技大学学报2024，Vol.44Issue(4)：409-415,7.DOI:10.16725/j.1673-808X.202391
基于重加权图拉普拉斯正则化的时变图信号重构算法
A time-varying graph signal reconstruction algorithm based on reweighted graph Laplacian regularization
摘要
Abstract
A time-varying signal reconstruction method based on Reweighted Graph Laplacian Regularization(Reweighted GLR)is proposed to solve the problem that the observed time-varying signals are missing due to noise pollution and machine malfunction,which leads to inaccurate results of the subsequent data processing.algorithm.Firstly,the algorithm constructs a graph model based on the spatial distance information of the data.Secondly,the time-varying graph signal reconstruction problem is summarized as an unconstrained optimization problem based on the spatial domain smoothing property of the time-varying graph signal in the graph model.Finally,the optimization problem is solved by using a reweighted iterative algorithm,which adjusts the edge weights as time changes and dynamically updates the graph Laplacian matrix,such that the inherent correlation of the data as it changes over time is portrayed and the time-space correlation of the time-varying graph signal is fully exploited.Simulation results show that the pro-posed algorithm further exploits the temporal correlation of time-varying graph signals,reducing reconstruction errors and improv-ing reconstruction performance compared with reconstruction algorithms based on the smoothness of the spatial domain graph of time-varying graph signals.关键词
图信号处理/时变图信号/信号重构/重加权/图拉普拉斯矩阵Key words
graph signal processing/time-varying graph signal/signal reconstruction/reweighted/graph Laplacian matrix分类
信息技术与安全科学引用本文复制引用
何丽梅,蒋俊正..基于重加权图拉普拉斯正则化的时变图信号重构算法[J].桂林电子科技大学学报,2024,44(4):409-415,7.基金项目
广西创新驱动发展专项(桂科AA21077008) （桂科AA21077008）
广西科技基地和人才专项(桂科AD21220112) （桂科AD21220112）
桂林电子科技大学研究生教育创新计划(2022YCXS039) （2022YCXS039）
