南京大学学报（自然科学版）2026，Vol.62Issue(4)：562-576,15.DOI:10.13232/j.cnki.jnju.2026.04.005
基于局部低秩分解的缺失标签多标记学习方法
Local low-rank decomposition for multi-label learning with missing labels
摘要
Abstract
Multi-label learning has found extensive application in practical scenarios such as text classification.However,in real-world settings,labels are often partially missing due to high annotation costs,human oversight,or complex data collection processes.Such incomplete supervision significantly impairs a model's ability to capture the underlying label structures.Most existing methods rely on a global low-rank assumption to model the label matrix and recover latent label structures.However,this assumption is often inadequate for capturing the complex and diverse relationships among labels.To address this limitation,we propose a Local Low-Rank Decomposition method for Multi-Label Learning with Missing Labels(LLRD-MLML).Our approach constructs label-correlated subsets and applies low-rank modeling to these subsets to uncover complex structural relationships among labels.Furthermore,we introduce a local-global joint learning mechanism that integrates the local models with a unified prediction model.To preserve the structural consistency among samples,a graph regularization constraint is incorporated.The resulting optimization problem is solved using an alternating optimization strategy.Experimental results on multiple public multi-label datasets demonstrate that the proposed method achieves superior performance and stability across various label missing ratios,thereby validating the effectiveness of local low-rank structure modeling in scenarios with missing labels.关键词
多标记学习/缺失标签/局部低秩分解/局部-全局学习/图正则化Key words
multi-label learning/missing labels/local low-rank decomposition/local-global learning/graph regularization分类
信息技术与安全科学引用本文复制引用
李纪蔚,陈琳琳,何强,王恒友..基于局部低秩分解的缺失标签多标记学习方法[J].南京大学学报（自然科学版）,2026,62(4):562-576,15.基金项目
国家自然科学基金(12301581,62573036),北京市自然科学基金(4252033) （12301581,62573036）
