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

• URL: http://arxiv.org/abs/2604.09064v1
• Date: Fri, 10 Apr 2026 07:44:46 GMT
• Title: Feature-Label Modal Alignment for Robust Partial Multi-Label Learning
• Abstract summary: We propose a novel PML method based on feature-label modal alignment (PML-MA)<n>PML-MA treats features and labels as two complementary modalities and restores their consistency through systematic alignment.<n>Experiments on both real-world and synthetic datasets demonstrate that PML-MA significantly outperforms state-of-the-art methods.
• Score: 66.1611192892514
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 restores their consistency through systematic alignment. Specifically, PML-MA first employs low-rank orthogonal decomposition to generate pseudo-labels that approximate the true label distribution by filtering noisy labels. It then aligns features and pseudo-labels through both global projection into a common subspace and local preservation of neighborhood structures. Finally, a multi-peak class prototype learning mechanism leverages the multi-label nature where instances simultaneously belong to multiple categories, using pseudo-labels as soft membership weights to enhance discriminability. By integrating modal alignment with prototype-guided refinement, PML-MA ensures pseudo-labels better reflect the true distribution while maintaining robustness against label noise. Extensive experiments on both real-world and synthetic datasets demonstrate that PML-MA significantly outperforms state-of-the-art methods, achieving superior classification accuracy and noise robustness.

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