Partial Label Learning via Conditional-Label-Aware Disambiguation
- Resource Type
- Authors
- Zhi-Gang Dai; Cuiping Li; Peng Ni; Hong Chen; Suyun Zhao
- Source
- Journal of Computer Science and Technology. 36:590-605
- Subject
- business.industry
Computer science
Feature vector
Supervised learning
Process (computing)
020207 software engineering
02 engineering and technology
Machine learning
computer.software_genre
Computer Science Applications
Theoretical Computer Science
ComputingMethodologies_PATTERNRECOGNITION
Computational Theory and Mathematics
Hardware and Architecture
Theory of computation
0202 electrical engineering, electronic engineering, information engineering
Leverage (statistics)
Artificial intelligence
Noise (video)
business
computer
Software
- Language
- ISSN
- 1860-4749
1000-9000
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.