Improving multi-label classification with missing labels by learning label-specific features

DOI10.1016/j.ins.2019.04.021zbMath1451.68225OpenAlexW2938834426WikidataQ128057710 ScholiaQ128057710MaRDI QIDQ2214997

Zhixiang Yuan, Xiao Zheng, Weigang Zhang, Zekai Cheng, Feng Qin, Jun Huang, Qingming Huang

Publication date: 10 December 2020

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2019.04.021

zbMATH Keywords

multi-label learningmissing labelslabel-specific features

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (7)

Bayesian network based label correlation analysis for multi-label classifier chain ⋮ Transformed Schatten-1 penalty based full-rank latent label learning for incomplete multi-label classification ⋮ Multi-label feature selection based on stable label relevance and label-specific features ⋮ Global and local attention-based multi-label learning with missing labels ⋮ Weak multi-label learning with missing labels via instance granular discrimination ⋮ Learning shared and non-redundant label-specific features for partial multi-label classification ⋮ Multi-label learning with missing and completely unobserved labels

Uses Software

• MULAN

Cites Work

• A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
• Robust non-negative sparse graph for semi-supervised multi-label learning with missing labels
• Multi-label classification using a fuzzy rough neighborhood consensus
• Multi-label semi-supervised classification through optimum-path forest
• A novel attribute reduction approach for multi-label data based on rough set theory
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