PCA expansion random variables
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Hello everyone.
Right now I am applying PCA to a set of observations. [coeffUV, score_vectorUV, latentUV, tsquaredUV, explainedUV, muUV]=pca(Z, 'Centered',false); being Z a gaussian correlation kernel.
As far as I understand, Score columns are the eigenfunctions. I have read in some books that if one multiplies the eigenfunctions (columns of score) by the origninal matrix data, gaussian random variables are obtained. Hower, if I write randvar=Z*score(:,1); and hist(randvar) I don't get a Gaussian histogram.
Can someone tell me what I am doing wrong?
Thanks.
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The columns of the coeff output are the eigenvectors, as explained in
doc pca
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2019-6-12
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