Graph-based semi-supervised learning via improving the quality of the graph dynamically

DOI10.1007/s10994-021-05975-yOpenAlexW3161522974MaRDI QIDQ2051324

Jie Wang, Wei Wei, Junbiao Cui, J. Y. Liang

Publication date: 24 November 2021

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-021-05975-y

zbMATH Keywords

clusteringsemi-supervised learninggraph constructionlabel inference

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (3)

Multi-view graph convolutional networks with attention mechanism ⋮ Neural predictor-based automated graph classifier framework ⋮ Hypergraph regularized semi-supervised support vector machine

Uses Software

• MixMatch

Cites Work

• Multiple graph regularized graph transduction via greedy gradient Max-Cut
• Towards a theoretical foundation for Laplacian-based manifold methods
• MixMatch
• Learning safe multi-label prediction for weakly labeled data
• Simple strategies for semi-supervised feature selection
• A survey on semi-supervised feature selection methods
• A survey on semi-supervised learning
• Introduction to Semi-Supervised Learning
• The Concave-Convex Procedure
• Label Information Guided Graph Construction for Semi-Supervised Learning
• Learning with $\ell ^{1}$-graph for image analysis
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