Partial Label Learning via Conditional-Label-Aware Disambiguation

作     者：Peng Ni Su-Yun Zhao Zhi-Gang Dai Hong Chen Cui-Ping Li Peng Ni;Su-Yun Zhao;Zhi-Gang Dai;Hong Chen;Cui-Ping Li

作者机构：Key Laboratory of Data Engineering and Knowledge Engineering(Renmin University of China)Ministry of Education Beijing 100087China School of InformationRenmin University of ChinaBeijing 100087China 

出 版 物：《Journal of Computer Science & Technology》 (计算机科学技术学报（英文版）)

年 卷 期：2021年第36卷第3期

页      面：590-605页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Key Research&Develop Plan of China under Grant Nos.2017YFB1400700 and 2018YFB1004401 the National Natural Science Foundation of China under Grant Nos.61732006,61702522,61772536,61772537,62076245,and 62072460 Beijing Natural Science Foundation under Grant No.4212022 

主　　题：disambiguation partial label learning similarity and dissimilarity weak supervision 

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 *** paper proposes a unified formulation that employs proper label constraints for training models while simultaneously performing *** 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 *** experiments on artificial and real-world partial label datasets show that our approach significantly outperforms state-of-the-art counterparts on classification prediction.