Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/12301
Title: Adaptive sign algorithm for graph signal processing
Authors: Yan, Yi
Kuruoğlu, Ercan Engin
Altınkaya, Mustafa Aziz
Tsinghua University
Tsinghua University
01. Izmir Institute of Technology
Keywords: Adaptive filter
Graph signal processing
Impulsive noise
Sign algorithm
Issue Date: Jun-2022
Publisher: Elsevier
Abstract: Efficient and robust online processing techniques for irregularly structured data are crucial in the current era of data abundance. In this paper, we propose a graph/network version of the classical adaptive Sign algorithm for online graph signal estimation under impulsive noise. The recently introduced graph adaptive least mean squares algorithm is unstable under non-Gaussian impulsive noise and has high computational complexity. The Graph-Sign algorithm proposed in this work is based on the minimum dispersion criterion and therefore impulsive noise does not hinder its estimation quality. Unlike the recently proposed graph adaptive least mean pth power algorithm, our Graph-Sign algorithm can operate without prior knowledge of the noise distribution. The proposed Graph-Sign algorithm has a faster run time because of its low computational complexity compared to the existing adaptive graph signal processing algorithms. Experimenting on steady-state and time-varying graph signals estimation utilizing spectral properties of bandlimitedness and sampling, the Graph-Sign algorithm demonstrates fast, stable, and robust graph signal estimation performance under impulsive noise modeled by alpha stable, Cauchy, Student's t, or Laplace distributions.
URI: https://doi.org/10.1016/j.sigpro.2022.108662
https://hdl.handle.net/11147/12301
Appears in Collections:Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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