Partial Label Learning via Conditional-Label-Aware Disambiguation
| Autor: | Zhi-Gang Dai, Cuiping Li, Peng Ni, Hong Chen, Suyun Zhao |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
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 |
| Zdroj: | Journal of Computer Science and Technology. 36:590-605 |
| ISSN: | 1860-4749 1000-9000 |
| DOI: | 10.1007/s11390-021-0992-x |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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