Multi-View Partial Multi-Label Learning with Graph-Based Disambiguation


  • Ze-Sen Chen Southeast University
  • Xuan Wu Alibaba Group
  • Qing-Guo Chen Alibaba Group
  • Yao Hu Alibaba Group
  • Min-Ling Zhang Southeast University



In multi-view multi-label learning (MVML), each training example is represented by different feature vectors and associated with multiple labels simultaneously. Nonetheless, the labeling quality of training examples is tend to be affected by annotation noises. In this paper, the problem of multi-view partial multi-label learning (MVPML) is studied, where the set of associated labels are assumed to be candidate ones and only partially valid. To solve the MVPML problem, a two-stage graph-based disambiguation approach is proposed. Firstly, the ground-truth labels of each training example are estimated by disambiguating the candidate labels with fused similarity graph. After that, the predictive model for each label is learned from embedding features generated from disambiguation-guided clustering analysis. Extensive experimental studies clearly validate the effectiveness of the proposed approach in solving the MVPML problem.




How to Cite

Chen, Z.-S., Wu, X., Chen, Q.-G., Hu, Y., & Zhang, M.-L. (2020). Multi-View Partial Multi-Label Learning with Graph-Based Disambiguation. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 3553-3560.



AAAI Technical Track: Machine Learning