Search Results for author: Shih-Gu Huang

Found 6 papers, 1 papers with code

Persistent Homological State-Space Estimation of Functional Human Brain Networks at Rest

1 code implementation1 Jan 2022 Moo K. Chung, Shih-Gu Huang, Ian C. Carroll, Vince D. Calhoun, H. Hill Goldsmith

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.

Clustering Graph Clustering +1

Revisiting convolutional neural network on graphs with polynomial approximations of Laplace-Beltrami spectral filtering

no code implementations26 Oct 2020 Shih-Gu Huang, Moo K. Chung, Anqi Qiu, Alzheimer's Disease Neuroimaging Initiative

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.

Classification General Classification

Fast Mesh Data Augmentation via Chebyshev Polynomial of Spectral filtering

no code implementations6 Oct 2020 Shih-Gu Huang, Moo K. Chung, Anqi Qiu, Alzheimer's Disease Neuroimaging Initiative

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.

Data Augmentation

Fast Polynomial Approximation of Heat Kernel Convolution on Manifolds and Its Application to Brain Sulcal and Gyral Graph Pattern Analysis

no code implementations7 Nov 2019 Shih-Gu Huang, Ilwoo Lyu, Anqi Qiu, Moo. K. Chung

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

no code implementations3 Jun 2017 Soo-Chang Pei, Shih-Gu Huang, Jian-Jiun Ding

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

no code implementations26 May 2017 Soo-Chang Pei, Shih-Gu Huang

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.

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