Label-specific feature selection and two-level label recovery for multi-label classification with missing labels

DOI10.1016/j.neunet.2019.04.011zbMath1434.68431DBLPjournals/nn/MaC19OpenAlexW2949481297WikidataQ93122400 ScholiaQ93122400MaRDI QIDQ2185625

Tommy W. S. Chow, Jianghong Ma

Publication date: 5 June 2020

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2019.04.011

zbMATH Keywords

multi-label learninglabel recoverymissing labellabel-specific feature selectiontwo-level semantic correlations

Mathematics Subject Classification ID

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

Related Items (2)

Label distribution feature selection for multi-label classification with rough set ⋮ Unsupervised feature selection via adaptive autoencoder with redundancy control

Uses Software

• BoosTexter

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
• BoosTexter: A boosting-based system for text categorization
• Discriminative sparse flexible manifold embedding with novel graph for robust visual representation and label propagation
• Joint Multilabel Classification With Community-Aware Label Graph Learning
• Multi-Modal Curriculum Learning for Semi-Supervised Image Classification
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• Unnamed Item

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