0b1af4d6-0cc7-45d8-a9a7-0fa992215d8020220121063057587naun:naunmdt@crossref.orgMDT DepositInternational Journal of Circuits, Systems and Signal Processing1998-446410.46300/9106http://www.naun.org/cms.action?id=30291720221720221610.46300/9106.2022.16https://npublications.com/journals/circuitssystemssignal/2022.phpHoujieLiCollege of Information and Communication Engineering, Dalian Minzu University, Dalian, ChinaMinYangCollege of Information and Communication Engineering, Dalian Minzu University, Dalian, ChinaYuZhouCollege of Information and Communication Engineering, Dalian Minzu University, Dalian, ChinaRuiruiZhengCollege of Information and Communication Engineering, Dalian Minzu University, Dalian, ChinaWenpengLiuCollege of Information and Communication Engineering, Dalian Minzu University, Dalian, ChinaJianjunHeCollege of Information and Communication Engineering, Dalian Minzu University, Dalian, ChinaPartial label learning is a new weak- ly supervised learning framework. In this frame- work, the real category label of a training sample is usually concealed in a set of candidate labels, which will lead to lower accuracy of learning al- gorithms compared with traditional strong super- vised cases. Recently, it has been found that met- ric learning technology can be used to improve the accuracy of partial label learning algorithm- s. However, because it is difficult to ascertain similar pairs from training samples, at present there are few metric learning algorithms for par- tial label learning framework. In view of this, this paper proposes a similar pair-free partial la- bel metric learning algorithm. The main idea of the algorithm is to define two probability distri- butions on the training samples, i.e., the proba- bility distribution determined by the distance of sample pairs and the probability distribution de- termined by the similarity of candidate label set of sample pairs, and then the metric matrix is ob- tained via minimizing the KL divergence of the two probability distributions. The experimental results on several real-world partial label dataset- s show that the proposed algorithm can improve the accuracy of k-nearest neighbor partial label learning algorithm (PL-KNN) better than the ex- isting partial label metric learning algorithms, up to 8 percentage points.1102022110202221522326https://npublications.com/journals/circuitssystemssignal/2022/a522005-026(2022).pdf10.46300/9106.2022.16.26https://npublications.com/journals/circuitssystemssignal/2022/a522005-026(2022).pdfV. C. Raykar, S. Yu, L. H. Zhao, G. H. Valadez, C. Florin, L. Bogoni, and L. Moy. 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