And this you have likely seen already in Wikipedia:https://en.wikipedia.org/wiki/Principal_component_analysis"...PCA is mostly used as a tool in exploratory data analysis and for making predictive models. It's often used to visualize genetic distance and relatedness between populations. PCA can be done by eigenvalue decomposition of a data covariance (or correlation) matrix or singular value decomposition of a data matrix, usually after mean centering[clarification needed] (and normalizing or using Z-scores) the data matrix for each attribute.[4] The results of a PCA are usually discussed in terms of component scores, sometimes called factor scores (the transformed variable values corresponding to a particular data point), and loadings (the weight by which each standardized original variable should be multiplied to get the component score)..."
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. ______________________________ _________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailma n/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
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