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.