关键词: 多标记学习, 分类, 标记相关性, 维度约简

Abstract: Multi-label learning, which considers the case of an object related to multiple labels, attracts much attention in recent years. Multi-label learning research aims to improve the performance of multi-label learning algorithms by reducing the complexity of the feature space and the label space. This paper systematically analyses the developments in multi-label learning research from four aspects including multi-label classification, label ranking, multi-label dimension reduction and label correlation and also points out the existing problems in the multi-label learning research. Finally, it summarizes several valuable research directions, which provides reference for the further research in this field.

Key words: multi-label learning, classification, label correlation, dimension reduction