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Discriminative adversarial networks for positive-unlabeled learning

3 June 2019
Hui Chen
Fangqing Liu
Yin Wang
Liyue Zhao
ArXiv (abs)PDFHTML
Abstract

As an important semi-supervised learning task, positive-unlabeled (PU) learning aims to learn a binary classifier only from positive and unlabeled data. In this article, we develop a novel PU learning framework, called discriminative adversarial networks, which contains two discriminative models represented by deep neural networks. One model Φ\PhiΦ predicts the conditional probability of the positive label for a given sample, which defines a Bayes classifier after training, and the other model DDD distinguishes labeled positive data from those identified by Φ\PhiΦ. The two models are simultaneously trained in an adversarial way like generative adversarial networks, and the equilibrium can be achieved when the output of Φ\PhiΦ is close to the exact posterior probability of the positive class. In contrast with existing deep PU learning approaches, DAN does not require the class prior estimation, and its consistency can be proved under very general conditions. Numerical experiments demonstrate the effectiveness of the proposed framework.

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