Thanks. Do you mean that if feature one has a larger derivation than feature two, after zscore they will have the same weight? In that case, it is a bias, right? The feature one should be more important than feature two in the PCA.
On Thu, May 24, 2018 at 5:09 PM, Michael Eickenberg < michael.eickenb...@gmail.com> wrote: > 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/applicatio >> ns/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 >> >> > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > >
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn