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.
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