6 papers
Spectral Graph Uncertainty Principles via the Graph Fractional Fourier Transform
Yu Zhang, Bing-Zhao Li
This paper develops a graph fractional uncertainty principle in the graph fractional Fourier transform (GFRFT) domain. We introduce localization operators in the vertex domain and…
Two-Channel Filter Banks on Joint Time-Vertex Graphs with Oversampled Graph Laplacian Matrix
Yu Zhang, Bing-Zhao Li
To address the limitations of conventional critically sampled graph filter banks in joint time-vertex signal processing, which require decomposing the joint graph into bipartite su…
Sampling of Graph Signals Based on Joint Time-Vertex Fractional Fourier Transform
Yu Zhang, Bing-Zhao Li
With the growing demand for non-Euclidean data analysis, graph signal processing (GSP) has gained significant attention for its capability to handle complex time-varying data. This…
Reconstruction of Graph Signals on Complex Manifolds with Kernel Methods
Yu Zhang, Linyu Peng, Bing-Zhao Li
Graph signals are widely used to describe vertex attributes or features in graph-structured data, with applications spanning the internet, social media, transportation, sensor netw…
The Graph Fractional Fourier Transform in Hilbert Space
Yu Zhang, Bing-Zhao Li
Graph signal processing (GSP) leverages the inherent signal structure within graphs to extract high-dimensional data without relying on translation invariance. It has emerged as a…
Discrete Linear Canonical Transform on Graphs: Uncertainty Principle and Sampling
Yu Zhang, Bing-Zhao Li
With an increasing influx of classical signal processing methodologies into the field of graph signal processing, approaches grounded in discrete linear canonical transform have fo…