Margin Based PU Learning


  • Tieliang Gong Xi'an Jiaotong University
  • Guangtao Wang University of Michigan
  • Jieping Ye University of Michigan
  • Zongben Xu Xi'an Jiaotong University
  • Ming Lin University of Michigan



PU Learning, Generalization error, Classification


The PU learning problem concerns about learning from positive and unlabeled data. A popular heuristic is to iteratively enlarge training set based on some margin-based criterion. However, little theoretical analysis has been conducted to support the success of these heuristic methods. In this work, we show that not all margin-based heuristic rules are able to improve the learned classifiers iteratively. We find that a so-called large positive margin oracle is necessary to guarantee the success of PU learning. Under this oracle, a provable positive-margin based PU learning algorithm is proposed for linear regression and classification under the truncated Gaussian distributions. The proposed algorithm is able to reduce the recovering error geometrically proportional to the positive margin. Extensive experiments on real-world datasets verify our theory and the state-of-the-art performance of the proposed PU learning algorithm.




How to Cite

Gong, T., Wang, G., Ye, J., Xu, Z., & Lin, M. (2018). Margin Based PU Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1).