Variable selection and interpretation in correlation principal components
Variable selection and interpretation in correlation principal components
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DOI：
10.1002/env.728
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发表时间：
2005-09-01
期刊：
影响因子：
1.7
通讯作者：
Jolliffe, IT
中科院分区：
文献类型：
--
作者：
Al-Kandari, NM;Jolliffe, IT
主成分分析（PCA）是一种降维工具，它用较少数量的衍生变量替换多变量数据集中的变量。通常进行降维以帮助解释数据集，但由于每个主成分通常涉及所有原始变量，因此PCA的解释仍然很困难。克服这一困难的一种方法是选择原始变量的子集，并使用该子集来近似主成分。本文回顾了一些技术，选择子集的变量，并检查其优点，在保留PCA中的信息，并在帮助解释的主要来源的变化的数据。版权所有（c）2005年约翰威利父子有限公司。
Principal component analysis (PCA) is a dimension-reducing tool that replaces the variables in a multivariate data set by a smaller number of derived variables. Dimension reduction is often undertaken to help in interpreting the data set but, as each principal component usually involves all the original variables, interpretation of a PCA can still be difficult. One way to overcome this difficulty is to select a subset of the original variables and use this subset to approximate the principal components. This article reviews a number of techniques for choosing subsets of the variables and examines their merits in terms of preserving the information in the PCA, and in aiding interpretation of the main sources of variation in the data. Copyright (c) 2005 John Wiley & Sons, Ltd.