基于双 HSIC和稀疏正则化的多标签特征选择.

To rationally utilize the sample information and label information in multi-label data and improve the classification performance of the model, multi-label feature selection (DHSR) via dual Hilbert-Schmidt independence criterion (HSIC) and sparse regularization was proposed. This method introduces dual HSIC as a regular term on the basis of linear mapping to enhance the dependency between pseudo-label space and feature space, and enhance the dependency between pseudo-label space and real label space, respectively. Moreover, L2.1 norm was used as a sparse regularity term to improve the generalization ability of the model and reduce the computational complexity of the model. Finally, the results of comparison experiments on several classical multi-label datasets verify the effectiveness and superiority of DHSR. [ABSTRACT FROM AUTHOR]