How do you choose the min and max for the min-max normalization on a histogram classifier?

Please let me know what to do when there is a value in the testing set is bigger than the max value used to min-max normalize the training set building a histogram classifier.

Do I go back and change the bounds of the min-max normalization for the training set? Wouldn’t that violate the notion that your training set should generalize to any testing set on its own and that you should retroactively change the what was done during the classifier building on the training set based on future testing sets that you are not supposed to know?

Do I change the bounds of the min-max normalization to the the min and max of the testing set? But, you are supposed to use the same transformation on the testing set as the training set, right?

Or, do I just let there be a bin on either side of the normalized histogram that such that everything that gets (not actually) normalized above (below) the interval [0,1] goes into the bin for all values (below) the interval?

Or, do I just exclude values that get transformed outside of the histogram’s interval?

None of these seem right. Please let me know if I am missing an option.