Is it necessary to normalize (Z-score) the dataset (high dimension) when the dimensionality of features varies greatly?
If I normalize the dataset, then the probability density (f1) obtained by KDE using the normalized dataset should not be equal to the probability density (f2) obtained by KDE using the dataset directly, so how to convert f1 to f2 after getting f1?