Weighted Contrastive Learning With Hard Negative Mining for Positive and Unlabeled Learning

Abstract

Positive and unlabeled (PU) learning aims to train a suitable classifier simply based on a set of positive data and unlabeled data. The state-of-the-art methods usually formulate PU learning as a cost-sensitive learning problem, in which every unlabeled example is treated as negative with modified class weights. However, existing methods fail to generate high-quality data representations, which brings about negative-prediction preference and performance decline. To overcome this problem, this article proposes a novel algorithm dubbed weighted contrastive learning with hard negative mining for positive and unlabeled learning (termed WConPU), which specifically designs a new prototypical contrastive strategy for gaining discriminative representations for PU learning. Specifically, our proposed WConPU consists of a contrastive learning (CL) module and a classifier training module, which can benefit from each other in an iterative manner. Moreover, a novel weighted contrastive objective function equipped with a prototype-based hard negative mining module is proposed to further enhance the representation quality. Theoretically, we show that our WConPU can be justified from the perspective of the expectation-maximization (EM) algorithm. Empirically, we compare our method with state-of-the-art PU algorithms on a wide range of real-world benchmark datasets, and the experimental results firmly demonstrate the advantage of our proposed method over the existing PU learning approaches.