Right order of doing feature selection, PCA and normalization?

Question

I know that feature selection helps me remove features that may have low contribution. I know that PCA helps reduce possibly correlated features into one, reducing the dimension

Answer 1

Generally speaking, Normalization is needed before PCA. The key to the problem is the order of feature selection, and it's depends on the method of feature selection.

A simple feature selection is to see whether the variance or standard deviation of the feature is small. If these values are relatively small, this feature may not help the classifier. But if you do normalization before you do this, the standard deviation and variance will become smaller (generally less than 1), which will result in very small differences in std or var between the different features.If you use zero-mean normalization, the mean of all the features will equal 0 and std equals 1.At this point, it might be bad to do normalization before feature selection

Feature selection is flexible, and there are many ways to select features. The order of feature selection should be chosen according to the actual situation