Results 41 to 50 of about 32,468 (150)

A Directed Graph Fourier Transform With Spread Frequency Components [PDF]

IEEE Transactions on Signal Processing, 2019
15 pages, 13 figures.
Rasoul Shafipour, Ali Khodabakhsh 0002, Gonzalo Mateos, Evdokia Nikolova   +3 more
openaire   +2 more sources

GSD: An R package for graph signal decomposition

SoftwareX
Graph signals residing on the vertices of a graph have recently gained prominence in research of various fields, including neural networks, social networks, traffic patterns, and sensors.
Hyeonglae Cho, Hee-Seok Oh, Donghoh Kim
doaj   +1 more source

Graph-Based EEG Signal Compression for Human–Machine Interaction

IEEE Access
Communication of bioelectric signals, such as electroencephalography (EEG) signals, will be a key technology for smooth interaction between users and remote robots.
Takuya Fujihashi, Toshiaki Koike-Akino
doaj   +1 more source

Region Adaptive Graph Fourier Transform for 3D Point Clouds [PDF]

2020 IEEE International Conference on Image Processing (ICIP), 2020
5 pages, 3 figures, accepted ICIP ...
Eduardo Pavez, Benjamin Girault, Antonio Ortega, Philip A. Chou   +3 more
openaire   +3 more sources

Graph Fractional Hilbert Transform: Theory and Application

Fractal and Fractional
The graph Hilbert transform (GHT) is a key tool in constructing analytic signals and extracting envelope and phase information in graph signal processing.
Daxiang Li, Zhichao Zhang
doaj   +1 more source

A social recommendation model based on adaptive residual graph convolution networks [PDF]

PeerJ Computer Science
Incorporating social information in the recommendation algorithm based on graph neural network (GNN) alleviates the data sparsity and cold-start problems to a certain extent, and effectively improves the recommendation performance of the model.
Rui Chen, Kangning Pang, Qingfang Liu, Lei Zhang, Hao Wu, Cundong Tang, Pu Li   +6 more
doaj   +2 more sources

On Sparse Graph Fourier Transform

, 2018
In this paper, we propose a new regression-based algorithm to compute Graph Fourier Transform (GFT). Our algorithm allows different regularizations to be included when computing the GFT analysis components, so that the resulting components can be tuned for a specific task.
Safavi, Seyed Hamid, Khatua, Manas, Cheung, Ngai-Man, Torkamani-Azar, Farah   +3 more
openaire   +2 more sources

Geary’s c and Spectral Graph Theory: A Complement

Mathematics, 2023
Spatial autocorrelation, which describes the similarity between signals on adjacent vertices, is central to spatial science, and Geary’s c is one of the most-prominent numerical measures of it.
Hiroshi Yamada
doaj   +1 more source

Graph Embedding in the Graph Fractional Fourier Transform Domain

CoRR
Spectral graph embedding plays a critical role in graph representation learning by generating low-dimensional vector representations from graph spectral information. However, the embedding space of traditional spectral embedding methods often exhibit limited expressiveness, failing to exhaustively capture latent structural features across alternative ...
Changjie Sheng, Zhichao Zhang, Wei Yao 0004   +2 more
openaire   +2 more sources

A short-graph fourier transform via personalized pagerank vectors [PDF]

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016
The short-time Fourier transform (STFT) is widely used to analyze the spectra of temporal signals that vary through time. Signals defined over graphs, due to their intrinsic complexity, exhibit large variations in their patterns. In this work we propose a new formulation for an STFT for signals defined over graphs.
Mariano Tepper, Guillermo Sapiro
openaire   +2 more sources