TY - JOUR
T1 - Windowed fractional Fourier transform on graphs
T2 - Properties and fast algorithm
AU - Yan, Fang Jia
AU - Li, Bing Zhao
N1 - Publisher Copyright:
© 2021 Elsevier Inc.
PY - 2021/11
Y1 - 2021/11
N2 - A key challenge in the field of graph signal processing is to design proper transform methods to extract valuable information from signals on weighted graphs. This paper first defines convolution, translation and modulation operators in graph fractional domain, and proposes windowed graph fractional Fourier transform (WGFRFT). Related properties are proved. Then, a fast algorithm of WGFRFT is designed to increase flexibility. The robustness and superiority of the fast algorithm are verified via simulations with synthetic signals on graphs. Finally, an application to graph clustering is presented to show the practicability of WGFRFT.
AB - A key challenge in the field of graph signal processing is to design proper transform methods to extract valuable information from signals on weighted graphs. This paper first defines convolution, translation and modulation operators in graph fractional domain, and proposes windowed graph fractional Fourier transform (WGFRFT). Related properties are proved. Then, a fast algorithm of WGFRFT is designed to increase flexibility. The robustness and superiority of the fast algorithm are verified via simulations with synthetic signals on graphs. Finally, an application to graph clustering is presented to show the practicability of WGFRFT.
KW - Fast algorithm
KW - Fractional Fourier transform
KW - Graph signal processing
KW - Windowed graph Fourier transform
UR - http://www.scopus.com/inward/record.url?scp=85113469480&partnerID=8YFLogxK
U2 - 10.1016/j.dsp.2021.103210
DO - 10.1016/j.dsp.2021.103210
M3 - Article
AN - SCOPUS:85113469480
SN - 1051-2004
VL - 118
JO - Digital Signal Processing: A Review Journal
JF - Digital Signal Processing: A Review Journal
M1 - 103210
ER -