Principal components analysis need standardization or normalization?
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