Partial multi-label feature selection via adaptive dual-graph regularization
Hao Xie, Ivy Liu, Bing Xue, Mengjie Zhang · Knowledge-Based Systems · 2025
Partial Multi-Label Learning (PML) tackles the challenge of developing accurate models based on candidate label sets that include both ground-truth labels and noisy ones. However, high-dimensional data often limits the performance of existing methods. Furthermore, traditional Multi-Label Feature Selection (MFS) methods face challenges in accurately identifying the optimum feature subset from partial multi-label data. To solve these issues, we propose a novel method, Partial Multi-label Feature Selection via Adaptive Dual-graph Regularization (PMFS-ADG). First, we leverage low-rank constraints and sparse representation to model the global relationships among labels, recovering the ground-truth label distribution from the original label space and distinguishing noisy labels. Then, adaptive dual-graph regularization is introduced to learn the non-linear geometric information of both the ground-truth label space and the feature space, enhancing label disambiguation while improving the discriminative ability of the selected features. The L 2 , 1 -norm is utilized to impose sparse constraints on the weight matrix, effectively removing irrelevant features. Furthermore, to ensure convergence, we design an efficient alternating optimization algorithm. Experimental results on both synthetic and genuine partial multi-label datasets demonstrate that the proposed method outperforms existing methods.