  Positive-Unlabeled (PU) learning tries to learn binary classifiers from a few
labeled positive examples with many unlabeled ones. Compared with ordinary
semi-supervised learning, this task is much more challenging due to the absence
of any known negative labels. While existing cost-sensitive-based methods have
achieved state-of-the-art performances, they explicitly minimize the risk of
classifying unlabeled data as negative samples, which might result in a
negative-prediction preference of the classifier. To alleviate this issue, we
resort to a label distribution perspective for PU learning in this paper.
Noticing that the label distribution of unlabeled data is fixed when the class
prior is known, it can be naturally used as learning supervision for the model.
Motivated by this, we propose to pursue the label distribution consistency
between predicted and ground-truth label distributions, which is formulated by
aligning their expectations. Moreover, we further adopt the entropy
minimization and Mixup regularization to avoid the trivial solution of the
label distribution consistency on unlabeled data and mitigate the consequent
confirmation bias. Experiments on three benchmark datasets validate the
effectiveness of the proposed method.Code available at:
https://github.com/Ray-rui/Dist-PU-Positive-Unlabeled-Learning-from-a-Label-Distribution-Perspective.
