1 citations · 1 across the 5 of their papers we have counts for
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…
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…
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…