Bringing Clustering to MLL: Weakly-Supervised Clustering for Partial Multi-Label Learning
Yu Chen, Weijun Lv, Yue Huang, Xuhuan Zhu et autres
Label noise in multi-label learning (MLL) poses significant challenges for model training, particularly in partial multi-label learning (PML) where candidate labels contain both relevant and irrelevant labels. While clustering offers a natural approach to exploit data structure for noise identification, traditional clustering …