Robust Partial Multi‐Label Learning Under Dual Noise via Joint Subspace Learning

ABSTRACT Partial Multi‐label Learning (PML) deals with the ambiguity where each instance is annotated with a set of candidate labels, and only a subset of which is valid. While existing PML methods focus primarily on label disambiguation, they often rely on the assumption of a clean feature space. However, in real‐world applications, data are frequently plagued by the co‐existence of label noise and feature noise, referred to as the dual noise challenge. Consequently, model robustness degrades substantially. To address this, we propose a framework named Ranking‐Consistent Correntropy‐based subspace learning for Partial Multi‐label Learning (RCC‐PML). Unlike existing dual noise PML methods that operate in the input space, our work introduces a subspace learning framework, where robust representation and semantic ranking are jointly optimized to enforce cross‐space consistency. Specifically, we leverage the Maximum Correntropy Criterion (MCC) to construct robust scatter matrices, effectively suppressing heavy‐tailed feature noise. To tackle label ambiguity, a ranking‐consistent constraint is introduced to encourage a reasonable margin between ground‐truth and false‐positive labels in the projected subspace. Furthermore, we incorporate dual‐graph regularization to preserve both the local manifold structure via anchor embedding and global semantic consistency. Finally, L 2,1 ‐norm regularization is imposed on the projection matrix to perform adaptive feature selection. Extensive experiments on benchmark datasets demonstrate that the proposed method significantly outperforms state‐of‐the‐art algorithms, particularly in heavy‐tailed environments.