复杂数据背景下的多标记学习研究进展OA

Multi-label learning is an important learning paradigm in the field of machine learning,which shows advanced performance in many applications.Data with the main characteristics of large-scale,incomplete information,multiple dimen-sions and dynamic evolution are bringing many challenges to multi-label learning.It is necessary to carry out a more refined design of the multi-label learning method according to the characteristics of different learning tasks under com-plex data environment.Therefore,this paper analyzes the source of the complexity of multi-label data and reviews the research progress of multi-label learning under complex data environment.Firstly,the paper introduces the problem defini-tion,learning form and development trend of multi-label learning in detail.Next,the complexity of multi-label data from three levels:label,feature and sample is discussed.Then,the summarized,elaborated and analyzed multi-label learning methods under complex data environment are provided.Finally,the challenges and the possible future application direc-tions of multi-label learning are summarized.