Hi, that totally depends on the nature of your data and whether the standard deviation of individual feature axes/columns of your data carry some form of importance measure. Note that PCA will bias its loadings towards columns with large standard deviations all else being held equal (meaning that if you have zscored columns, and then you choose one column and multiply it by, say 1000, then that component will likely show up as your first component [if 1000 is comparable or large wrt the number of features you are using])
Does this help? Michael On Thu, May 24, 2018 at 4:39 PM, Shiheng Duan <shid...@ucdavis.edu> wrote: > Hello all, > > I wonder is it necessary or correct to do z score transformation before > PCA? I didn't see any preprocessing for face image in the example of Faces > recognition example using eigenfaces and SVMs, link: > http://scikit-learn.org/stable/auto_examples/applications/plot_face_ > recognition.html#sphx-glr-auto-examples-applications- > plot-face-recognition-py > > I am doing on a similar dataset and got a weird result if I standardized > data before PCA. The components figure will have a strong gradient and it > doesn't make any sense. Any ideas about the reason? > > Thanks. > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > >
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