Results 181 to 190 of about 9,987 (195)

Hypergraph Learning: From Algorithms to Applications

Graphs are a general language for describing and modeling interconnected systems. To learn graph data, Graph Neural Networks (GNNs) have been introduced. However, traditional graph data structures often fall short of describing the higher-order complex relationships within these systems.
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Feature Learning Using Spatial-Spectral Hypergraph Discriminant Analysis for Hyperspectral Image

IEEE Transactions on Cybernetics, 2019
Fulin Luo, BO DU, Liangpei Zhang
exaly  

Adaptive Hypergraph Learning and its Application in Image Classification

IEEE Transactions on Image Processing, 2012
Jun Yu, Dacheng Tao, Meng Wang
exaly  

Joint hypergraph learning and sparse regression for feature selection

Pattern Recognition, 2017
Zhihong Zhang, Lu Bai, Edwin R Hancock
exaly  

Inductive Multi-Hypergraph Learning and Its Application on View-Based 3D Object Classification

IEEE Transactions on Image Processing, 2018
Zizhao Zhang, Rongrong Ji, Yue Gao
exaly  

A Survey on Hypergraph Representation Learning

ACM Computing Surveys
Alessia Antelmi, Gennaro Cordasco, Mirko Polato   +2 more
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Learning to Map Social Network Users by Unified Manifold Alignment on Hypergraph

IEEE Transactions on Neural Networks and Learning Systems, 2018
Wei Zhao, Shulong Tan, Ziyu Guan
exaly  

Cross-Modality Microblog Sentiment Prediction via Bi-Layer Multimodal Hypergraph Learning

IEEE Transactions on Multimedia, 2019
Rongrong Ji, Fuhai Chen, Liujuan Cao
exaly  

Collaborative contrastive learning for hypergraph node classification

Pattern Recognition
NuoSi Li, Michael Ng, Jinyi Long
exaly  

EduCross: Dual adversarial bipartite hypergraph learning for cross-modal retrieval in multimodal educational slides

Information Fusion
Ming Li, Yuting Chen
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