Robust non-negative sparse graph for semi-supervised multi-label learning with missing labels
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Publication:781016
DOI10.1016/j.ins.2017.08.061zbMath1436.68311OpenAlexW2750785001MaRDI QIDQ781016
Tommy W. S. Chow, Jianghong Ma
Publication date: 16 July 2020
Published in: Information Sciences (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.ins.2017.08.061
Learning and adaptive systems in artificial intelligence (68T05) Graph theory (including graph drawing) in computer science (68R10)
Related Items (8)
Bayesian network based label correlation analysis for multi-label classifier chain ⋮ Label distribution feature selection for multi-label classification with rough set ⋮ HesGCN: Hessian graph convolutional networks for semi-supervised classification ⋮ Label-specific feature selection and two-level label recovery for multi-label classification with missing labels ⋮ Improving multi-label classification with missing labels by learning label-specific features ⋮ TPNE: topology preserving network embedding ⋮ Weighted co-association rate-based Laplacian regularized label description for semi-supervised regression ⋮ A constrained least squares regression model
Uses Software
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