This question is about using PCA as a dimension reduction method before feeding the data into a classifier. It's a common procedure to use PCA for a data set which contains a large number of features, and to only use the first several PCA-scores instead of the original features. My question is: After the PCA score has been extracted should I need to re-scale them ? (as the scores are in descending order...and can be in different magnitude)
Rescaling is always a good idea. As pointed out by jb. in some cases it won't make a different, but in some cases it will make a significant difference.
Let me add that for PCA related stuff, sometimes subtle differences in the rescaling can make a relatively large difference. Consider evaluating the following alternatives: unit length normalization, linear re-scaling, mean variance rescaling and rank scaling.