Multi-label learning with missing labels using mixed dependency graphs

DOI10.1007/s11263-018-1085-3zbMath1458.68187arXiv1804.00117OpenAlexW2964181345WikidataQ125854760 ScholiaQ125854760MaRDI QIDQ2200028

Bernard Ghanem, Fan Jia, Baoyuan Wu, Siwei Lyu, Wei Liu

Publication date: 15 September 2020

Published in: International Journal of Computer Vision (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1804.00117

zbMATH Keywords

image retrievalmulti-label learningimage annotationmissing labelsmixed dependency graphs

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Computing methodologies for image processing (68U10) Information storage and retrieval of data (68P20)

Related Items

Transformed Schatten-1 penalty based full-rank latent label learning for incomplete multi-label classification, Global and local attention-based multi-label learning with missing labels, Weak multi-label learning with missing labels via instance granular discrimination, Scalable and Flexible Unsupervised Feature Selection

Uses Software

• TagProp
• ML-KNN
• ImageNet
• RASL

Cites Work

• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• On the global and linear convergence of direct extension of ADMM for 3-block separable convex minimization models
• ML-KNN: A lazy learning approach to multi-label learning
• Multilabel classification via calibrated label ranking
• Optimal Parameter Selection for the Alternating Direction Method of Multipliers (ADMM): Quadratic Problems
• Introduction to Information Retrieval
• Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
• The direct extension of ADMM for multi-block convex minimization problems is not necessarily convergent
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