Consider that you have performed Principal Component Analysis of a centered and unscaled data set, that is, the variance-covariance matrix to be analysed is not equal to the correlation matrix. To do the PCA for the same set, but now centered and scaled, Select one: O a. reuse the eigenvalues, with the only change is to rescale them to add to the number of components. The eigenvectors are the same. O b. it is not possible to reuse eigenvalues nor eigenvectors. О с. in some selected instances we can reuse eigenvalues and eigenvectors of the analysis of centered and unscaled data.
Consider that you have performed Principal Component Analysis of a centered and unscaled data set, that is, the variance-covariance matrix to be analysed is not equal to the correlation matrix. To do the PCA for the same set, but now centered and scaled, Select one: O a. reuse the eigenvalues, with the only change is to rescale them to add to the number of components. The eigenvectors are the same. O b. it is not possible to reuse eigenvalues nor eigenvectors. О с. in some selected instances we can reuse eigenvalues and eigenvectors of the analysis of centered and unscaled data.
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Transcribed Image Text:Consider that you have performed Principal Component Analysis of a centered and unscaled data set, that is, the variance-covariance matrix to be analysed is not equal to the correlation matrix. To
do the PCA for the same set, but now centered and scaled,
Select one:
a. reuse the eigenvalues, with the only change is to rescale them to add to the number of components. The eigenvectors are the same.
O b.
it is not possible to reuse eigenvalues nor eigenvectors.
in some selected instances we can reuse eigenvalues and eigenvectors of the analysis of centered and unscaled data.
C.
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