Graph Band-limited Signals Reconstruction Method Based Graph Spectral Domain Shifting

被引：0
|
作者：
Yang J. [1 ]
Zhao L. [1 ]
Guo W.-B. [1 ,2 ]
机构：
[1] Beijing University of Posts and Telecommunications, Beijing
[2] Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory, Shijiazhuang
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2021年 / 47卷 / 09期
基金：
中国国家自然科学基金;
关键词：
Graph signal processing; Graph spectral theory; Shift operator; Signal reconstruction;
D O I：
10.16383/j.aas.c200802
中图分类号：
学科分类号：
摘要：
Aiming at the problem of graph band-limited signals reconstruction, a novel reconstruction model based shift strategy in graph spectral domain is proposed in this paper, and it models the identity invariance of graph spectral band-limited components as a least-square problem. For solving the established reconstruction model, two novel reconstruction methods are proposed based on spectral shift operator and residual spectral shift operator. Compared with other methods, the novel methods improve iteration efficiency and reconstruction accuracy. Besides, the novel methods are suitable for the problem of separate band-limited graph signals reconstruction and have good performances. The simulation shows that compared with other reconstruction methods of band-limited graph signals, the novel methods improve about seventy percent in iterative efficiency and sixty percent of reconstruction accuracy. Copyright © 2021 Acta Automatica Sinica. All rights reserved.
引用
收藏
页码：2132 / 2142
页数：10
相关论文
共 30 条
  • [1] Liu Jian-Wei, Li Hai-En, Luo Xiong-Lin, Learning technique of probabilistic graphical models: A review, Acta Automatica Sinica, 40, 6, pp. 1025-1044, (2014)
  • [2] Shuman D I, Narang S K, Frossard P, Ortega A, Vandergheynst P, The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains, IEEE Signal Processing Magazine, 30, 3, pp. 83-98, (2013)
  • [3] Tremblay N, Gonalves P, Borgnat P., Design of graph filters and filterbanks, Cooperative and Graph Signal Processing, pp. 299-324, (2018)
  • [4] Chen S H, Varma R, Sandryhaila A, Kovacevic J, Discrete signal processing on graphs: Sampling theory, IEEE Transactions on Signal Processing, 63, 24, pp. 6510-6523, (2015)
  • [5] Hu W, Cheung G, Ortega A, Au O C, Multiresolution graph Fourier transform for compression of piecewise smooth images, IEEE Transactions on Image Processing, 24, 1, pp. 419-433, (2014)
  • [6] Liu Y L, Yang L S, You K Y, Guo W B, Wang W B, Graph learning based on spatiotemporal smoothness for time-varying graph signal, IEEE Access, 7, pp. 62372-62386, (2019)
  • [7] Jiang Jun-Zheng, Yang Jie, Ouyang Shan, Novel method for outlier nodes detection and localization in wireless sensor networks, Journal of Electronics and Information Technology, 40, 10, pp. 2358-2364, (2018)
  • [8] Yang Jie, Jiang Jun-Zheng, Method for data recovery in the sensor network based on the joint graph model, Journal of Xidian University, 47, pp. 44-51, (2020)
  • [9] Liao Xiang-Wen, Chen Xing-Jun, Wei Jing-Jing, Chen Guo-Long, Cheng Xue-Qi, A multi-layer relation graph model for extracting opinion targets and opinion words, Acta Automatica Sinica, 43, 3, pp. 462-471, (2017)
  • [10] Zhang Jian-Peng, Pei Yu-Long, Liu Cong, Li Shao-Mei, Chen Hong-Chang, A semi-supervised clustering algorithm based on factor graph model for dynamic graphs, Acta Automatica Sinica, 46, pp. 670-680, (2020)
123