基于双HSIC和稀疏正则化的多标签特征选择

Multi-label feature selection via dual HSICand sparse regularization

为了合理地利用多标签数据中的样本信息和标签信息,提高模型的分类性能,提出了基于双希尔伯特-施密特独立性准则(Hilbert-Schmidt independence criterion,HSIC)和稀疏正则化的多标签特征选择(DHSR)。该方法在线性映射的基础上引入双HSIC作为正则项,增强伪标签空间和特征空间之间的依赖关系,增强伪标签空间和真实标签空间之间的依赖关系。并使用L2,1范数作为稀疏正则项,以提高模型的泛化能力和减少模型的计算复杂度。最后,在多个经典多标签数据集上的对比实验结果验证了DHSR的有效性和优越性。

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,L 2,1 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.