I know that it is good practice to perform normalization (subtracting the data by its mean and dividing it by its standard deviation) first on the training data, and in a later step to use the mean and standard deviation of the training data to normalize also the test data.
I am aware from feature selection, that the feature selection for supervised feature selection methods - i.e. feature selection methods which do make use of the class labels - must be done solely based on the training set. However, since normalization does not make use of any class labels, I wonder how the common practice described above is justified?