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Regular Paper

Partial Label Learning via Conditional-Label-Aware Disambiguation

Key Laboratory of Data Engineering and Knowledge Engineering (Renmin University of China), Ministry of Education Beijing 100087, China
School of Information, Renmin University of China, Beijing 100087, China
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Abstract

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.

Keywords

disambiguationpartial label learningsimilarity and dissimilarityweak supervision

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Journal of Computer Science and Technology
Volume 36 Issue 3,
May 2021
Pages 590-605
DOI: 10.1007/s11390-021-0992-x
Cite this article:
Ni P, Zhao S-Y, Dai Z-G, et al. Partial Label Learning via Conditional-Label-Aware Disambiguation. Journal of Computer Science and Technology, 2021, 36(3): 590-605. https://doi.org/10.1007/s11390-021-0992-x

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Received: 15 September 2020
Accepted: 22 April 2021
Published: 05 May 2021
©Institute of Computing Technology, Chinese Academy of Sciences 2021
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