Positive-Unlabeled Learning With Label Distribution Alignment

doi:10.1109/TPAMI.2023.3319431

	Positive-Unlabeled Learning With Label Distribution Alignment
	Jiang, Yangbangyan 1; Xu, Qianqian 2; Zhao, Yunrui 1; Yang, Zhiyong 1; Wen, Peisong 2,3; Cao, Xiaochun 4; Huang, Qingming 1,2,5
	2023-12-01
发表期刊	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
ISSN	0162-8828
卷号	45 期号:12 页码:15345-15363
摘要	Positive-Unlabeled (PU) data arise frequently in a wide range of fields such as medical diagnosis, anomaly analysis and personalized advertising. The absence of any known negative labels makes it very challenging to learn binary classifiers from such data. Many state-of-the-art methods reformulate the original classification risk with individual risks over positive and unlabeled data, and explicitly minimize the risk of classifying unlabeled data as negative. This, however, usually leads to classifiers with a bias toward negative predictions, i.e., they tend to recognize most unlabeled data as negative. In this paper, we propose a label distribution alignment formulation for PU learning to alleviate this issue. Specifically, we align the distribution of predicted labels with the ground-truth, which is constant for a given class prior. In this way, the proportion of samples predicted as negative is explicitly controlled from a global perspective, and thus the bias toward negative predictions could be intrinsically eliminated. On top of this, we further introduce the idea of functional margins to enhance the model's discriminability, and derive a margin-based learning framework named Positive-Unlabeled learning with Label Distribution Alignment (PULDA). This framework is also combined with the class prior estimation process for practical scenarios, and theoretically supported by a generalization analysis. Moreover, a stochastic mini-batch optimization algorithm based on the exponential moving average strategy is tailored for this problem with a convergence guarantee. Finally, comprehensive empirical results demonstrate the effectiveness of the proposed method.
关键词	Estimation Stochastic processes Optimization Computer science Predictive models Information processing Fasteners Positive-unlabeled learning weakly supervised learning binary classification
DOI	10.1109/TPAMI.2023.3319431
收录类别	SCI
语种	英语
资助项目	National Key R&D Program of China[2018AAA0102000] ; National Natural Science Foundation ofChina[62236008] ; National Natural Science Foundation ofChina[U21B2038] ; National Natural Science Foundation ofChina[61931008] ; National Natural Science Foundation ofChina[62025604] ; National Natural Science Foundation ofChina[6212200758] ; National Natural Science Foundation ofChina[61976202] ; National Natural Science Foundation ofChina[62206264] ; Fundamental Research Funds for the Central Universities ; Youth Innovation Promotion Association CAS ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB28000000] ; Innovation Funding of ICT, CAS[E000000]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:001130146400080
出版者	IEEE COMPUTER SOC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/38378
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Xu, Qianqian; Huang, Qingming
作者单位	1.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 101408, Peoples R China 2.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Comp Sci Technol, Beijing 101408, Peoples R China 4.Sun Yat Sen Univ, Sch Cyber Sci & Technol, Shenzhen Campus, Shenzhen 518107, Peoples R China 5.Univ Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management BDKM, Beijing 101408, Peoples R China
推荐引用方式 GB/T 7714	Jiang, Yangbangyan,Xu, Qianqian,Zhao, Yunrui,et al. Positive-Unlabeled Learning With Label Distribution Alignment[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2023,45(12):15345-15363.
APA	Jiang, Yangbangyan.,Xu, Qianqian.,Zhao, Yunrui.,Yang, Zhiyong.,Wen, Peisong.,...&Huang, Qingming.(2023).Positive-Unlabeled Learning With Label Distribution Alignment.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,45(12),15345-15363.
MLA	Jiang, Yangbangyan,et al."Positive-Unlabeled Learning With Label Distribution Alignment".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 45.12(2023):15345-15363.