[00619] Optimal Transport for Positive and Unlabeled Learning
Session Time & Room : 4E (Aug.24, 17:40-19:20) @F403
Type : Contributed Talk
Abstract : Positive and unlabeled learning (PUL) aims to train a binary classifier based on labeled positive samples and unlabeled Samples, which is challenging due to the unavailability of negative training samples. This talk will introduce a novel optimal transport model with a regularized marginal distribution for PUL. By using the Frank-Wolfe algorithm, the proposed model can be solved properly. Extensive experiments showed that the proposed model is effective and can be used in meteorological applications.