论文标题
使用单个判别函数在高维度中的顺序线性判别分析
Sequential Linear Discriminant Analysis in High Dimensions Using Individual Discriminant Functions
论文作者
论文摘要
在过去的二十年中，强调了高维分类，并进行了许多研究，以规避高维度中遇到的挑战。虽然现有方法主要集中于制定分类规则，假设协变量独立或对样本协方差矩阵或样本平均值或其他方法进行正规化或其他方式，但我们提出了一种新的方法，该方法采用每个协变量的“歧视能力”的新方法，选择一个重要的变量，选择一个最算法的变量，从而构建了最佳的类别，并构建了最佳分类的类别，并构建了构建型号。我们进行仿真研究并分析实际数据集，以通过将其与现有分类器进行比较来说明我们提出的分类器的性能。
High dimensional classification has been highlighted for last two decades and much research has been conducted in order to circumvent challenges encountered in high dimensions. While existing methods have focused mainly on developing classification rules assuming independence of covariates or using regularization on the sample covariance matrix or the sample mean vector or among others, we propose a novel approach that employs the "discriminatory power" of each covariate, selects a set of important variables yielding the lowest misclassification rate empirically, and constructs the optimal linear classifier with selected variables. We carry out simulation studies and analyze real data sets to illustrate the performance of our proposed classifier by comparing it with existing classifiers.
