Discriminative Feature Selection (DFS) is an algorithm, proposed recently for effective feature selection by considering both joint linear discriminant analysis and row sparsity regularization. However, this method is not robust enough to protect the data from outliers, because it utilizes the squared ${\mathcal{L}}_2$-norm distance metric. To overcome this problem, we present in this paper, a novel discriminative feature selection algorithm, which uses the robust ${\mathcal{L}}_{2,1}$-norm for measuring the distances in DFS. Although the algorithm is apparently simple, it should not be considered trivial because of its non-convexity. Also, we present an analysis of the convergence, both theoretical and empirical. More importantly, we proposed an iterative algorithm to achieve optimal results. Experimental results, using various data sets, demonstrate the effectiveness of the proposed method.

鉴别特征选择（DFS）是最近提出的一种有效算法，该算法同时考虑了联合线性鉴别分析和行稀疏性正则化。但是，此方法不足以保护数据免受异常影响，因为它利用平方${\mathcal{大号}}_2$-标准距离指标。为了克服这个问题，我们在本文中提出了一种新颖的区分特征选择算法，该算法使用了鲁棒性${\mathcal{大号}}_{2，\mathrm{1个}}$-norm用于测量DFS中的距离。尽管该算法看似简单，但由于其不具有凸性，因此不应被认为是微不足道的。另外，我们对收敛性进行了分析，无论是理论上还是经验上。更重要的是，我们提出了一种迭代算法来获得最佳结果。使用各种数据集的实验结果证明了该方法的有效性。