Abstract:Multi-label learning dimensionality reduction method has been a research hotspot in the practical application problem to deal with data sets with higher features dimensions, labels dimensions or both dimensions. In view of the large number of multilabel learning dimensionality reduction methods and the lack of scientific classification, from the perspective of the dimension reduction space selection of multilabel data, a multilabel learning dimension reduction method is proposed to be classified into three types according to feature space dimension reduction, label space dimension reduction and both. The feature space dimension reduction is divided into two categories: feature dimension reduction and feature selection. They are based on the independent and dependent space of each other. The research status of typical multilabel learning dimensionality reduction algorithms is summarized. Finally, the research status and inspiration of multilabel learning dimensionality reduction methods are reviewed, and further research directions are proposed for the future.