I estimated a Partial Least Squares model where the X matrix had normalized columns. Now I want to predict the value for a new instance (which is a frequency vector summing to one.) I assume that if I just use the raw frequency values, the predicted value won't be on the same scale as the scenario where my 'new' instance was taken from the normalized X matrix. (i.e. Comparing the fitted values of the model with predicted value of new instance.)
I was thinking of adding the new instance as the bottom row of the original non-normalized X matrix, normalizing, and then using the values from this new bottom row to predict.
Alternatively, I could standardize by using the column means and standard deviations from the original non-normalized X.
Is one method preferred to the other? Is there a better way?