Abstract
Weakly supervised multi-label learning (WSML)
concentrates on a more challenging multi-label
classification problem, where some labels in the
training set are missing. Existing approaches make
multi-label prediction by exploiting the incomplete
logical labels directly without considering the relative importance of each label to an instance. In this
paper, a novel two-stage strategy named Weakly Supervised Multi-label Learning via Label Enhancement (WSMLLE) is proposed to learn from weakly
supervised data via label enhancement. Firstly, the
relative importance of each label, i.e., the description degrees are recovered by leveraging the structural information in the feature space and local correlations learned from the label space. Then, a tailored multi-label predictive model is induced by
learning from the training instances with the recovered description degrees. To our best knowledge, it
is the first attempt to unify the complement of the
missing labels and the recovery of the description
degrees into the same framework. Extensive experiments across a wide range of real-world datasets
clearly validate the superiority of the proposed approach