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Semisupervised Hypergraph Discriminant Learning for Dimensionality Reduction of Hyperspectral Image

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
Semisupervised learning is an effective technique to represent the intrinsic features of a hyperspectral image (HSI), which can reduce the cost to obtain the labeled information of samples.
Fulin Luo, Tan Guo, Zhiping Lin, Jinchang Ren, Xiaocheng Zhou   +4 more
doaj   +3 more sources

Adaptive dynamic hypergraph learning for ingredient aware food recommendation [PDF]

Scientific Reports
Food recommendation systems face fundamental challenges in modeling the complex, compositional relationships among users, foods, and ingredients. Traditional collaborative filtering and Graph Neural Networks rely on pairwise connections that oversimplify
Yazeed Alkhrijah, Abbas N. Talib, Narinderjit Singh Sawaran Singh, Ashraf Abed Hussein   +3 more
doaj   +2 more sources

Hypergraph and Uncertain Hypergraph Representation Learning Theory and Methods

Mathematics, 2022
With the advent of big data and the information age, the data magnitude of various complex networks is growing rapidly. Many real-life situations cannot be portrayed by ordinary networks, while hypergraphs have the ability to describe and characterize ...
Liyan Zhang, Jingfeng Guo, Jiazheng Wang, Jing Wang, Shanshan Li, Chunying Zhang   +5 more
doaj   +3 more sources

S2-HGNN: Scale-Aware Hypergraph Node Classification with Spectral Inductive Bias [PDF]

Entropy
Existing methods for hypergraph node classification usually rely on local message passing and use a unified strategy for topological modeling across hyperedges of different sizes. However, they have two limitations in semi-supervised settings.
Jiangnan Zhou, Sheng Zhang, Bing Wu, Qiuming Wang, Chennan Wu, Ziqiang Luo, Ka Sun, Hongmei Mao   +7 more
doaj   +2 more sources

Hypergraph learning for identification of COVID-19 with CT imaging [PDF]

Medical Image Analysis, 2021
Bin Song, Fei Shan, Zhongxiang Ding
exaly   +2 more sources

Multimodal Data Fusion Algorithm Based on Hypergraph Regularization [PDF]

Jisuanji kexue, 2023
The multi-modal data fusion improves the performance of data classification and prediction by learning the correlation information and complementary information between multiple datasets.However,existing data fusion methods are based on feature pattern ...
CUI Bingjing, ZHANG Yipu, WANG Biao
doaj   +1 more source

Multi-Hypergraph Learning-Based Brain Functional Connectivity Analysis in fMRI Data [PDF]

IEEE Transactions on Medical Imaging, 2020
Junqi Wang, Li Xiao, Vince Calhoun
exaly   +2 more sources

Prediction of Graduation Development Based on Hypergraph Contrastive Learning With Imbalanced Sampling

IEEE Access, 2023
With the increasingly competitive job market, the employment issue for college graduates has received more and more attention. Predicting graduation development can help students understand their suitable graduation development, thus easing the pressure ...
Yong Ouyang, Tuo Feng, Rong Gao, Yubin Xu, Jinghang Liu   +4 more
doaj   +1 more source

Multi-order hypergraph convolutional networks integrated with self-supervised learning

Complex & Intelligent Systems, 2023
Hypergraphs, as a powerful representation of information, effectively and naturally depict complex and non-pair-wise relationships in the real world. Hypergraph representation learning is useful for exploring complex relationships implicit in hypergraphs.
Jiahao Huang, Fangyuan Lei, Jianjian Jiang, Xi Zeng, Ruijun Ma, Qingyun Dai   +5 more
doaj   +1 more source

Signal Contrastive Enhanced Graph Collaborative Filtering for Recommendation

Data Science and Engineering, 2023
Graph collaborative filtering methods have shown great performance improvements compared with deep neural network-based models. However, these methods suffer from data sparsity and data noise problems.
Zhi-Yuan Li, Man-Sheng Chen, Yuefang Gao, Chang-Dong Wang   +3 more
doaj   +1 more source