In this paper, we introduce the novel concept of Graph Convolutional Networks (GCN) for brain analysis in populations, combining imaging and non-imaging data. We represent populations as a sparse graph where its vertices are associated with image-based feature vectors and the edges encode ...
In recent years, graph convolutional network has been widely used in hyperspectral image classification because of its feature aggregation mechanism, which can simultaneously represent the features of a single node and neighboring nodes. However, there a
Truong ND, Nguyen AD, Kuhlmann L, Bonyadi MR, Yang J, Kavehei O (2017) A generalised seizure prediction with convolutional neural networks for intracranial and scalp electroencephalogram data analysis. arXiv preprint arXiv:1707.01976 Hu W, Cao J, Lai X, Liu J (2019) Mean amplitude spectrum...
In this paper, we introduce the novel concept of Graph Convolutional Networks (GCN) for brain analysis in populations, combining imaging and non-imaging data. We represent populations as a sparse graph where its vertices are associated with image-based feature vectors and the edges encode ...
Graph convolutional networks for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 2020, 59, 5966–5978. [Google Scholar] [CrossRef] Wu, C.; Du, B.; Zhang, L. Hyperspectral anomalous change detection based on joint sparse representation. ISPRS J. Photogramm. Remote Sens. ...
Spectral-Spatial Graph Convolutional Networks for Semel-Supervised Hyperspectral Image Classification. IEEE Trans. Geosci. Remote Sens. Lett. 2019, 16, 241–245. [Google Scholar] [CrossRef] Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A.N.; Kaiser, L.; ...
graph convolutional networksIn recent years, convolution neural networks (CNNs) and graph convolution networks (GCNs) have been widely used in hyperspectral image classification (HSIC). CNNs can effectively extract the spatial spectral features of hyperspectral images (HSIs), while GCNs can quickly ...
Graph Comparative Learning (GCL) is a self-supervised method that combines the advantages of Graph Convolutional Networks (GCNs) and comparative learning, making it promising for learning node representations. However, the GCN encoders used in these methods rely on the Fourier transform to learn fixe...
Spectral clustering algorithm is an increasingly popular data clustering method, which derives from spectral graph theory. Spectral clustering builds the a... H Wang,J Chen - International Conference on Computational Aspects of Social Networks 被引量: 2发表: 2010年 Sciences de l'artificiel, modélisa...
Pattern Augmented Lightweight Convolutional Neural Network for Intrusion Detection System As the world increasingly becomes more interconnected, the demand for safety and security is ever-increasing, particularly for industrial networks. This ha... YE Tadesse,YJ Choi - Electronics (2079-9292) 被引量:...