Abstract
This chapter considers classical and robust principal component analysis (PCA). Principal component analysis is used to explain the dispersion structure with a few linear combinations of the original variables, called principal components. These linear combinations are uncorrelated if \(\varvec{S}\) or \(\varvec{R}\) is used as the dispersion matrix. The analysis is used for data reduction and interpretation.
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Olive, D.J. (2017). Principal Component Analysis. In: Robust Multivariate Analysis. Springer, Cham. https://doi.org/10.1007/978-3-319-68253-2_6
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DOI: https://doi.org/10.1007/978-3-319-68253-2_6
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Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68251-8
Online ISBN: 978-3-319-68253-2
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