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 a...