Abstract
The original purpose of PCA was to reduce a large number (p) of variables to a much small number (m) of PCs whilst retaining as much as possible of the variation in the p original variables. The technique is especially useful if m « p,and if the m PCs can be readily interpreted.
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© 1986 Springer Science+Business Media New York
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Jolliffe, I.T. (1986). Principal Components as a Small Number of Interpretable Variables: Some Examples. In: Principal Component Analysis. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-1904-8_4
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DOI: https://doi.org/10.1007/978-1-4757-1904-8_4
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4757-1906-2
Online ISBN: 978-1-4757-1904-8
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