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

TL;DR

This paper introduces PML-MA, a novel partial multi-label learning method that aligns features and labels as modalities, using pseudo-labels and prototype learning to improve robustness and accuracy.

Contribution

The paper proposes a new feature-label modal alignment approach with pseudo-label filtering and prototype learning for robust partial multi-label classification.

Findings

PML-MA outperforms existing methods on real-world datasets.

It achieves higher accuracy and noise robustness.

Pseudo-label filtering improves label quality.

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.