Orthogonal canonical variates for discrimination and classification
Orthogonal canonical variates for discrimination and classification
复制标题
用于区分和分类的正交典型变量
DOI：
10.1002/cem.1180090608
复制
发表时间：
1995
影响因子：
2.4
通讯作者：
W. Krzanowski
中科院分区：
文献类型：
--
作者：
W. Krzanowski
提出了一种新的衍生变量集，用于在判别分析之前对这些数据进行预处理，以显示多变量数据中的组分离。该技术结合了典型变量分析和主成分分析的最佳特征：衍生变量是原始变量的线性组合，这些变量优化了典型变量标准（组间与组内方差的比率），但受到主成分的正交性约束。在这个公式中，即使群内矩阵是奇异的（即当数据矩阵中的变量多于对象时），正则变量也可以导出。提出了一种提取这些变量的简单计算算法。在几个数据集上说明了这些方法，并与主成分分析和偏最小二乘等替代技术进行了比较。
A new set of derived variables is proposed for exhibiting group separation in multivariate data on for preprocessing such data prior to discriminant analysis. The technique combines optimal features of canonical variate analysis and principal component analysis: the derived variables are linear combinations of the original variables that optimize the canonical variate criterion (ratio of between‐group to within‐group variance) but subject to the orthogonality constraints of principal components. In this formulation the canonical variates can be derived even when the within‐group matrix is singular (i.e. when there are more variables than objects in the data matrix). A simple computational algorithm for extraction of these variables is proposed. The methods are illustrated on several data sets and compared with alternative techniques such as principal component analysis and partial least squares.