期刊
AMERICAN STATISTICIAN
卷 58, 期 1, 页码 72-76出版社
AMER STATISTICAL ASSOC
DOI: 10.1198/0003130042863
关键词
linear prediction; principal components; rotated components; simple components
Principal components are the benchmark for linear dimension reduction, but they are not always easy to interpret. For this reason, some alternatives have been proposed in recent years. These methods produce components that, unlike principal components, are correlated and/or have nonorthogonal loadings. This article shows that the criteria commonly used to evaluate principal components are not adequate for evaluating such alternatives, and proposes two new criteria that are more suitable for this purpose.
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