Principal components analysis need standardization or normalization? Principal components analysis need standardization or normalization?
After some google, I get confused. pca need the scalar be same. So which should I use.
Which technique needs to do before PCA?
Does pca need standardization?  standardized values will always be zero, and the standard deviation will always be one.
Does pca need normalization? range zero to one
or both ?
I believe Normalization refers to scaling the variable in between 0 and 1. Standardization refers to making the empirical distribution $Y\sim N(0,1)$. Principal component analysis, and similar methods such as Ridge Regression and Partial Least Squares regression, require standardization before training, i.e. $y_{i}=\frac{y_i-\mu_y}{\sigma_{y}}$, reference: Elements of Statistical Learning, Ch. 3.4