Bidirectional Single-Class Learning for Partial Multi-Label Learning

Partial multi-label learning (PML) is a special case of multi-label learning, in which label noise is confined to the observed positive labels. Although numerous PML methods have been developed to handle noisy label spaces, many of them rely on general noise disambiguation mechanisms without explicitly exploiting the asymmetric noise structure of PML. To better leverage this characteristic, we propose Bidirectional Single-Class Learning for Partial Multi-Label Learning (BSCPML), which treats positive and negative labels separately. Specifically, clean negative labels can be directly fitted through single-class learning, whereas positive labels may require purification before learning. To theoretically justify this asymmetric treatment, we develop a unified loss estimation framework that compares several representative PML losses in terms of bias, variance, and their combined estimation error. The analysis demonstrates that an appropriately weighted BSCPML loss can minimize the gap to the clean-label loss. Extensive experiments on 12 benchmark datasets demonstrate that BSCPML achieves competitive or superior performance compared with state-of-the-art PML methods, particularly under severe label noise.