基于局部低秩分解的缺失标签多标记学习方法OA

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.