10 papers
Optimal Sampling and Reconstruction of Graph Signals in the Fractional Fourier Domain
Xiaopeng Cheng, Zhichao Zhang, Yangfan He
Graph signal sampling and reconstruction are commonly formulated in the graph Fourier transform (GFT) domain. However, the reconstruction performance may be limited when practical…
Generalized Linear Graph Representation: A Compact Operator Space for Graph Signal Processing and Graph Neural Networks
Feiyue Zhao, Zhichao Zhang, Yangfan He
Graph Signal Processing (GSP) and Graph Neural Networks (GNNs) rely fundamentally on the matrix representation of the underlying graph topology. This representation defines key ope…
Rotation-Parameterized Graph Fractional Fourier Transform: Definition, Properties, and Optimal Filtering
Feiyue Zhao, Mingzhi Wang, Yangfan He +1
Graph spectral representations are fundamental in graph signal processing, providing a rigorous frameworkforanalyzing graph-structured data. The graph fractional Fourier transform…
Node-Oriented Proactive Spectral Modulation: A Unified Fractional Framework for Graph Signal Denoising
Manjun Cui, Zhichao Zhang, Yangfan He
Graph signal denoising is a fundamental task in graph signal processing. While the node-oriented filtering approach enhances spatial adaptability, it suffers from spectral rigidity…
MPFSR-Enhanced GNNs: Spectral Graph Neural Networks Enhancement Through Learnable Multiple-Parameter Graph Fractional Fourier Transforms
Manjun Cui, Xiaopeng Cheng, Yangfan He +1
Graph neural networks (GNNs) excel in processing non-Euclidean data, but traditional spectral GNNs rely on static bases and fundamentally lack active spectral regulation. Although…
Least-Squares Adaptive Filter-Based Cohen's Class Time-Frequency Distribution for Signal Denoising
Manjun Cui, Zhichao Zhang, Yangfan He
Inspired by the use of adaptive kernel-based Cohen's class time-frequency distributions (CCTFDs) for cross-term suppression, this paper aims to explore novel adaptive kernel functi…