Results 1 to 10 of about 9,987 (195)

Multimodal Feature Fusion Based Hypergraph Learning Model. [PDF]

Comput Intell Neurosci, 2022
Hypergraph learning is a new research hotspot in the machine learning field. The performance of the hypergraph learning model depends on the quality of the hypergraph structure built by different feature extraction methods as well as its incidence matrix.
Yang Z, Xu L, Zhao L.
europepmc   +3 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   +2 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

Multi-stakeholder News Recommendation Using Hypergraph Learning [PDF]

ECML PKDD 2020 WorkshopsWorkshops of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2020): SoGood 2020, 2020
AbstractRecommender systems are meant to fulfil user preferences. Nevertheless, there are multiple examples where users are not the only stakeholder in a recommendation platform. For instance, in news aggregator websites apart from readers, one can consider magazines (news agencies) or authors as other stakeholders.
Gharahighehi A, Vens C, Pliakos K.
europepmc   +4 more sources

Multi-Hypergraph Learning for Incomplete Multimodality Data. [PDF]

IEEE J Biomed Health Inform, 2018
Multi-modality data convey complementary information that can be used to improve the accuracy of prediction models in disease diagnosis. However, effectively integrating multi-modality data remains a challenging problem, especially when the data are incomplete.
Liu M, Liu M, Gao Y, Yap PT, Shen D.
europepmc   +4 more sources

A lightweight single-view contrastive learning hypergraph neural network for food–microbe–disease association prediction [PDF]

BMC Bioinformatics
Background Identifying potential associations among food, gut microbiota and disease is fundamental for elucidating interaction mechanisms and advancing personalized healthy dietary strategies. While computational methods have been extensively applied to
Jianqiang Hu, Mingyi Hu, Yangxiang Wu, Songyao Mu, Dahao Huang, Baolong Wang, Yuchen Gao, Shixin Gu, Jinlin Zhu   +8 more
doaj   +2 more sources

Hypergraph Representation Learning with Weighted- and Clustering-Biased Random Walks [PDF]

Entropy
Hypergraphs are powerful tools for modeling complex systems because they naturally encode higher-order interactions. However, most existing hypergraph representation-learning methods still struggle to capture such high-order structures, particularly in ...
Li Liang, Shi-Ming Cai, Shi-Cai Gong
doaj   +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

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
openaire   +4 more sources

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.
Zhang, Jiying, Chen, Yuzhao, Xiao, Xi, Lu, Runiu, Xia, Shu-Tao   +4 more
openaire   +3 more sources