A Positive-Unlabeled Learning Approach with Self-correcting Regularized Risk

The primary objective of Positive Unlabeled learning (PU learning) is to train a binary classifier with positively labeled data and unlabeled data. An inherent aspect of this approach involves incorporating the positive class prior of unlabeled data directly into the classification process, which is typically challenging in real-world scenarios. Moreover, existing studies often lack evaluations of PU classifiers without involving the positive class prior (true or estimated) of the unlabeled data, representing a significant research gap. In this paper, we introduce a robust, two-step PU learning algorithm by incorporating a potential negative sampler in step 1 (warm start) and minimizing a self-correcting regularized risk function in step 2. The risk function possesses a self-correcting property that attempts to mitigate the weakness of the potential negative sampler in the warm start step. The risk function enables us to enhance robustness in the presence of mislabeled candidate negative samples. We demonstrate the effectiveness of our method on image as well as text benchmarks. Results show that the proposed method consistently outperforms the state-of-the-art PU learning algorithms.