Abstract
Principal component analysis is often used as a dimension-reducing technique within some other type of analysis. For example, Chapter 8 described the use of PCs as regressor variables in a multiple regression analysis. The present chapter discusses three multivariate techniques, namely discriminant analysis, cluster analysis and canonical correlation analysis; for each of these three techniques, examples are given in the literature which use PCA as a dimension-reducing technique.
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© 1986 Springer Science+Business Media New York
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Jolliffe, I.T. (1986). Principal Components Used with Other Multivariate Techniques. In: Principal Component Analysis. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4757-1904-8_9
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DOI: https://doi.org/10.1007/978-1-4757-1904-8_9
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4757-1906-2
Online ISBN: 978-1-4757-1904-8
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