Partial Label Learning with Discrimination Augmentation

Partial label learning is a weakly supervised learning framework where each training example is associated with multiple candidate labels, among which only one is valid. Existing works on partial label learning mainly focus on classification model induction by disambiguating candidate label sets in the output space. Nevertheless, the feature representations of partial label training examples may be less informative of the ground-truth labels, which may result in negative influences on the disambiguation process. To circumvent this difficulty, the first attempt towards discrimination augmentation for partial label learning is investigated in this paper. The feature space is enriched with confidence-rated class prototype features to replenish discriminative characteristics of the underlying ground-truth labels for partial label training examples. Specially, an optimization formulation is proposed to jointly optimize the class prototype and estimate the labeling confidence over partial label training examples, which enforces both global consistency in the feature space and local consistency in the label space. We show that the class prototypes and the labeling confidence can be solved via alternating optimization. Extensive experiments on synthetic as well as real-world data sets validate the effectiveness of the proposed approach for improving the generalization performance of state-of-the-art partial label learning algorithms.