Graph-Regularized Low-Rank Label Correlation Learning with Label-Specific Features for Missing Labels
Missing labels are common in multi-label learning and can bias both label-correlation estimation and classifier induction. Existing missing-label methods often recover incomplete supervision mainly through global label correlations. However, correlation-driven recovery alone may produce over-smoothed supervision when annotations are sparse, while label-specific discriminative evidence may be weakened. To address this problem, we propose GLCS, a graph-regularized low-rank correlation learning framework with label-specific features for multi-label learning with missing labels. GLCS first uses the observed entries as reliable supervision sources and propagates them through a learned label correlation matrix. It then jointly learns sparse label-specific predictors, low-rank label correlations, and a label graph regularizer induced by the learned correlations. In this way, global label dependencies, local label-structure consistency, and label-wise discriminative features are optimized in a unified objective. The resulting problem is solved by an alternating proximal optimization scheme with soft thresholding for sparse predictors and singular value thresholding for low-rank correlations. Experiments on twelve benchmark datasets under three missing-label ratios show that GLCS obtains strong average performance across AP, AUC, CV, HL, OE, and RL, especially under high missing rates.