Joint time-vertex linear canonical transform

被引:2
|
作者
Zhang, Yu [1 ,2 ,3 ]
Li, Bing-Zhao [1 ,2 ]
机构
[1] Beijing Inst Technol, Sch Math & Stat, Beijing 100081, Peoples R China
[2] Beijing Inst Technol, Beijing Key Lab MCAACI, Beijing 100081, Peoples R China
[3] Keio Univ, Dept Mech Engn, Yokohama 2238522, Japan
关键词
Graph signal processing; Linear canonical transform; Joint time-vertex Fourier transform; Graph linear canonical transform; Dynamic mesh; SIGNAL; SERIES;
D O I
10.1016/j.dsp.2024.104728
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The emergence of graph signal processing (GSP) has spurred a deep interest in signals that naturally reside on irregularly structured data kernels, such as those found in social, transportation, and sensor networks. Recently, concepts and applications related to time-varying graph signal analysis have matured, linking temporal signal processing techniques with innovative tools in GSP. In this paper, similar to the extension of the graph fractional Fourier transform to the graph linear canonical transform, we define the joint time-vertex linear canonical transform (JLCT) and its properties. This transformation extends the joint time-vertex Fourier transform (JFT) and fractional Fourier transform (JFRFT), broadening the Fourier analysis in both time and vertex domains into the domain of the linear canonical transform (LCT). This offers an enhanced set of the LCT analysis tools for joint time-vertex processing. Applications of the JLCT in dynamic mesh denoising, clustering, and energy compactness demonstrate that JLCT can enhance regression and learning tasks, and can refine and improve the performance of the JFT and the JFRFT.
引用
收藏
页数:14
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