Partial multi-label green learning with global attention disambiguation

Partial multi-label learning (PML) aims to learn from samples annotated with incomplete label sets, where only a subset of the ground-truth labels is available. This problem becomes particularly challenging in resource-constrained environments, where model efficiency and interpretability are essential. In this paper, we propose a novel framework, Partial Multi-label Green Learning with Global Attention Disambiguation (PMGL-GAD), which integrates lightweight unsupervised feature extraction with global attention-guided disambiguation. Specifically, we adopt the green learning architecture PixelHop++ to construct multi-scale hierarchical features in a feed-forward, training-free manner. To enhance discriminative capability for partial label resolution, we introduce a Global Attention Disambiguation (GAD) module into the green learning pipeline. The GAD module builds a cross-channel data subspace via global attention mechanisms and a learnable gating strategy, enabling feature reweighting across labels without deep network training. Furthermore, a disambiguated label subspace is learned concurrently to improve label assignment. Experimental results demonstrate that PMGL-GAD achieves competitive performance with significantly reduced complexity, offering a promising direction for interpretable, low-carbon multi-label learning in weakly supervised scenarios.