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[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.
  • Classification : 49Q22, 68T01
  • Format : Talk at Waseda University
  • Author(s) :
    • Jie ZHANG (University of Hong Kong)
    • Yuguang YAN (Guangdong University of Technology)
    • Michael Ng (University of Hong Kong)