Say you split your data into two sets: training and test sets. You know that the inputs of your data are in [lower_bounds, upper_bounds]. Now, assume that you would like to do a min-max normalization on your inputs between $[0, 1]$. For the values of the max and the min, should you use the min/max of your learning dataset or the bounds [lower_bounds, upper_bounds]?
In the same way, in order to normalize your test set, you should use the same bounds as the ones used for the learning dataset. If you use the min/max of your training set, some of your values in the test set can be found outside of $[0, 1]$, if, for instance, some values of the test set are greater than the max of the data in the learning dataset. Is it an issue?