Partial Label Learning via Conditional-Label-Aware Disambiguation

• 作者: Peng Ni Su-Yun Zhao Zhi-Gang Dai Hong Chen Cui-Ping Li
• 来源期刊: 计算机科学技术学报（英文版）
• 年，卷（期）: 2021,（3）
• 所属期刊栏目: Special Section on Learning from Small Samples
• 页码范围: 590-605
• 页数: 16页
• 语种: 英文

摘要

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.