Here are more reference involving the "score" that may help you:
https://stats.stackexchange.com/questions/222/what-are-principal-component-scores
https://stats.stackexchange.com/questions/202578/what-is-the-meaning-of-the-variable-scores-in-matlabs-pca
ftp://statgen.ncsu.edu/pub/thorne/molevoclass/AtchleyOct19.pdf
On Saturday, May 26, 2018, 10:10:32 PM PDT, Shiheng Duan
<shid...@ucdavis.edu> wrote:
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/s
table/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.
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