摘要
本文利用矩阵的扫描运算，提出一种对高维随机向量 Ｘ＝（ｘ１，ｘ２，…，ｘｐ）’进行降维处理的实用方法—主变量筛选方法，给出了该方法的理论依据、直观解释、算法及数值例子．该方法是不同于主成分分析法的一种降维方法．特别，当变量Ｘ多重相关性突出时，本文方法效果显著．
A practical method, called selecting principal variables' method, that reduces the dimensions of a high dimensional random vector X =(x1, x2,… Xp)', with the sweep operation of matrix, is presented in this paper. The theoretical foundation, audio-visual explanation, algorithm and numerical example of the method are given. The method differs from one of the principal component analysis. Especially,the advantages of the method are marked, while the variables X's multicollinearity being serious.
出处
《应用数学学报》
CSCD
北大核心
2002年第1期101-107,共7页
Acta Mathematicae Applicatae Sinica
关键词
主变量
多重相关性
线性表示
筛选方法
扫描运算
随机向量
Principal variables, multicollinearity, linear expression, selecting method, sweep operation
