  Positive-unlabeled learning refers to the process of training a binary
classifier using only positive and unlabeled data. Although unlabeled data can
contain positive data, all unlabeled data are regarded as negative data in
existing positive-unlabeled learning methods, which resulting in diminishing
performance. We provide a new perspective on this problem -- considering
unlabeled data as noisy-labeled data, and introducing a new formulation of PU
learning as a problem of joint optimization of noisy-labeled data. This
research presents a methodology that assigns initial pseudo-labels to unlabeled
data which is used as noisy-labeled data, and trains a deep neural network
using the noisy-labeled data. Experimental results demonstrate that the
proposed method significantly outperforms the state-of-the-art methods on
several benchmark datasets.
