I am doing a machine learning project where I need to do
- then K-means clustering,
- then One class SVM
It seems all those procedure requires data scaling. Should I (A) scale my data just before PCA, or should I (B) scale my data every time I encounter a new procedure?
(B) will be like scaling the data before PCA, then scaling the data again before K-means,then scaling the data again before One class SVM. I am feeling (B) may lose the original information because I have been scaling it too many times.