论文标题
顶点判别分析的特征选择
Feature Selection for Vertex Discriminant Analysis
论文作者
论文摘要
我们从近端距离算法的角度重新访问顶点判别分析（VDA）。通过将稀疏集指定为直接控制活动功能数量的约束，VDA能够拟合具有不超过$ k $ Active功能的多类分类器。我们将稀疏的VDA方法与重复的交叉验证相结合，以在给定数据集上的整个模型尺寸上拟合分类器。我们的数值示例表明，直接与稀疏性抓斗是在高维环境中建立模型的一种有吸引力的方法。还考虑了基于内核VDA的应用。
We revisit vertex discriminant analysis (VDA) from the perspective of proximal distance algorithms. By specifying sparsity sets as constraints that directly control the number of active features, VDA is able to fit multiclass classifiers with no more than $k$ active features. We combine our sparse VDA approach with repeated cross validation to fit classifiers across the full range of model sizes on a given dataset. Our numerical examples demonstrate that grappling with sparsity directly is an attractive approach to model building in high-dimensional settings. Applications to kernel-based VDA are also considered.
