A Novel Perspective for Positive-Unlabeled Learning via Noisy Labels
This addresses a key bottleneck in binary classification with limited labeled data, offering a novel perspective that improves performance over existing methods.
The paper tackles the problem of positive-unlabeled learning by reframing unlabeled data as noisy-labeled data and optimizing jointly, resulting in a method that significantly outperforms state-of-the-art approaches on benchmark datasets.
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.