Results 21 to 30 of about 2,491 (218)
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
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Multi-Hypergraph Learning-Based Brain Functional Connectivity Analysis in fMRI Data. [PDF]
IEEE Trans Med Imaging, 2020 Xiao L +8 more
europepmc +2 more sources
Learning on Hypergraphs with Sparsity [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020 Hypergraph is a general way of representing high-order relations on a set of objects. It is a generalization of graph, in which only pairwise relations can be represented. It finds applications in various domains where relationships of more than two objects are observed.
Nguyen, Canh Hao, Mamitsuka, Hiroshi
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Learnable Hypergraph Laplacian for Hypergraph Learning
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022 HyperGraph Convolutional Neural Networks (HGCNNs) have demonstrated their potential in modeling high-order relations preserved in graph structured data. However, most existing convolution filters are localized and determined by the pre-defined initial hypergraph topology, neglecting to explore implicit and long-ange relations in real-world data.
Jiying Zhang +4 more
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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 +4 more
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Actual construction cost prediction using hypergraph deep learning techniques
Advanced Engineering InformaticsAccurate construction cost estimation at early stages is critical to enable project stakeholders to make financial decisions (e.g., set up the project budget).
Mingkai Li, Jack C P Cheng, Liqiao Xia
exaly +2 more sources
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 +5 more
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, 2023 This open access book discusses the theory and methods of hypergraph computation. Many underlying relationships among data can be represented using graphs, for example in the areas including computer vision, molecular chemistry, molecular biology, etc ...
Gao, Yue, Dai, Qionghai
core +1 more source
A Survey on Hypergraph Representation Learning
ACM Computing Surveys, 2023 Hypergraphs have attracted increasing attention in recent years thanks to their flexibility in naturally modeling a broad range of systems where high-order relationships exist among their interacting parts. This survey reviews the newly born hypergraph representation learning problem, whose goal is to learn a function to project objects—most commonly ...
Alessia Antelmi +5 more
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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 +3 more
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