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Found 6 papers, 1 papers with code
Persistent Homological State-Space Estimation of Functional Human Brain Networks at Rest
We introduce an innovative, data-driven topological data analysis (TDA) technique for estimating the state spaces of dynamically changing functional human brain networks at rest.
Revisiting convolutional neural network on graphs with polynomial approximations of Laplace-Beltrami spectral filtering
This paper revisits spectral graph convolutional neural networks (graph-CNNs) given in Defferrard (2016) and develops the Laplace-Beltrami CNN (LB-CNN) by replacing the graph Laplacian with the LB operator.
Fast Mesh Data Augmentation via Chebyshev Polynomial of Spectral filtering
Even though graph convolutional neural network (graph-CNN) has been widely used in deep learning, there is a lack of augmentation methods to generate data on graphs or surfaces.
Fast Polynomial Approximation of Heat Kernel Convolution on Manifolds and Its Application to Brain Sulcal and Gyral Graph Pattern Analysis
We also derive the closed-form expression of the spectral decomposition of the Laplace-Beltrami operator and use it to solve heat diffusion on a manifold for the first time.
Discrete Gyrator Transforms: Computational Algorithms and Applications
Besides, we propose a kind of DGT based on the eigenfunctions of the gyrator transform.
Two-dimensional nonseparable discrete linear canonical transform based on CM-CC-CM-CC decomposition
Simulation results show that the proposed methods have higher accuracy, lower computational complexity and smaller error in the additivity property compared with the previous works.
