Multi-label learning is facing great challenges due to high-dimensional feature space, complex label correlations and noises in multi-label data. Feature selection techniques have attracted considerable attention to address the problems. In this paper, we design our method based on dual-graph regularization, i.e., feature graph regularization and label graph regularization. The feature graph regularization is used to preserve the geometric structure of features, while label graph regularization intends to explore the correlations of labels. Furthermore, the $l_{2,1}$-norm is imposed on the loss function to enhance the robust of feature selection methods. As a result, a new feature selection method termed Robust Multi-label Feature Selection based on Dual-graph (DRMFS) is proposed. Particularly, only one unknown variable, feature weight matrix, is incorporated in our proposed method, which can reach global optimum. Additionally, we impose both $l_{2,1}$-norm and non-negative constraints onto the feature weight matrix to enhance the property of row-sparse. Finally, we design an optimization scheme to solve the proposed method, and offer the convergence proof of the optimization scheme. Extensive experimental results demonstrate the superiority of the proposed method in comparison to the-state-of-art multi-label feature selection methods. Finally, some insightful discussions with respect to the convergence analysis, complexity analysis and parameter sensitivity analysis are presented.

由于高维特征空间，复杂的标签相关性以及多标签数据中的噪声，多标签学习面临着巨大的挑战。特征选择技术已经吸引了相当大的注意力来解决这些问题。在本文中，我们设计基于双图正则化的方法，即特征图正则化和标签图正则化。特征图正则化用于保留特征的几何结构，而标签图正则化旨在探索标签的相关性。此外，${升}_{2，\mathrm{1个}}$-norm应用于损失函数，以增强特征选择方法的鲁棒性。因此，提出了一种新的特征选择方法，称为基于双图的鲁棒多标签特征选择（DRMFS）。特别是，在我们提出的方法中仅合并了一个未知变量，即特征权重矩阵，该变量可以达到全局最优。此外，我们同时强加${升}_{2，\mathrm{1个}}$-标准和非负约束到特征权重矩阵上，以增强行稀疏性。最后，我们设计了一种优化方案来解决该方法，并提供了优化方案的收敛性证明。大量的实验结果表明，与最新的多标签特征选择方法相比，该方法具有优越性。最后，就收敛性分析，复杂度分析和参数敏感性分析进行了有见地的讨论。