We use cookies to improve your experience with our site.
Peng Ni, Su-Yun Zhao, Zhi-Gang Dai, Hong Chen, Cui-Ping Li. Partial Label Learning via Conditional-Label-Aware Disambiguation[J]. Journal of Computer Science and Technology, 2021, 36(3): 590-605. DOI: 10.1007/s11390-021-0992-x
Citation: Peng Ni, Su-Yun Zhao, Zhi-Gang Dai, Hong Chen, Cui-Ping Li. Partial Label Learning via Conditional-Label-Aware Disambiguation[J]. Journal of Computer Science and Technology, 2021, 36(3): 590-605. DOI: 10.1007/s11390-021-0992-x

Partial Label Learning via Conditional-Label-Aware Disambiguation

  • Partial label learning is a weakly supervised learning framework in which each instance is associated with multiple candidate labels, among which only one is the ground-truth label. This paper proposes a unified formulation that employs proper label constraints for training models while simultaneously performing pseudo-labeling. Unlike existing partial label learning approaches that only leverage similarities in the feature space without utilizing label constraints, our pseudo-labeling process leverages similarities and differences in the feature space using the same candidate label constraints and then disambiguates noise labels. Extensive experiments on artificial and real-world partial label datasets show that our approach significantly outperforms state-of-the-art counterparts on classification prediction.
  • loading

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return