Feature-Label Modal Alignment for Robust Partial Multi-Label Learning

ArXi:2604.09064v1 Announce Type: new In partial multi-label learning (PML), each instance is associated with a set of candidate labels containing both ground-truth and noisy labels. The presence of noisy labels disrupts the correspondence between features and labels, degrading classification performance. To address this challenge, we propose a novel PML method based on feature-label modal alignment (PML-MA), which treats features and labels as two complementary modalities and res their consistency through systematic alignment.