PCA(Principal Component Analysis)is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. So this can be used for dimension reduction, features for any applications and etc. Moreover, I made it into 2 stages so you can find more detailed features and their reconstructions.
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