Regularized linear discriminant analysis and its application in microarrays
Regularized linear discriminant analysis and its application in microarrays
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DOI：
10.1093/biostatistics/kxj035
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发表时间：
2007-01-01
期刊：
影响因子：
2.1
通讯作者：
Tibshirani, Robert
中科院分区：
文献类型：
--
作者：
Guo, Yaqian;Hastie, Trevor;Tibshirani, Robert
本文引入了一种改进的线性判别分析，称为“萎缩质心正则化判别分析”（SCRDA）。该方法将“最近收缩质心”（NSC）的概念（Tibshirani等人，2003）推广到经典判别分析中。scda方法是专门为高维低样本情况下的分类问题而设计的，例如微阵列数据。通过模拟数据和实际数据，表明该方法在多变量分类问题中表现良好，通常优于PAM方法（使用NSC算法），并且可以与支持向量机分类器相媲美。它也适用于特征消除，可作为基因选择方法。该方法的开源R包（名为“rda”）可在CRAN （http://www.r-project.org）上下载和测试。
In this paper, we introduce a modified version of linear discriminant analysis, called the "shrunken centroids regularized discriminant analysis" (SCRDA). This method generalizes the idea of the "nearest shrunken centroids" (NSC) (Tibshirani and others, 2003) into the classical discriminant analysis. The SCRDA method is specially designed for classification problems in high dimension low sample size situations, for example, microarray data. Through both simulated data and real life data, it is shown that this method performs very well in multivariate classification problems, often outperforms the PAM method (using the NSC algorithm) and can be as competitive as the support vector machines classifiers. It is also suitable for feature elimination purpose and can be used as gene selection method. The open source R package for this method (named "rda") is available on CRAN (http://www.r-project.org) for download and testing.