I am wondering how I can manage a test data after using PCA or normalization and another thing like that in the classification because our model works on the representation given by its input vectors. For example, suppose you have used PCA in your training dataset to gain better accuracy or you have normalized (min-max) data. Now, you have developed a model and want to install it and label the new coming samples. You need to somehow apply PCA to each coming record and normalize that record. Applying PCA to one record will not yield the same effect of the PCA used in training phase and I think even it doesn't make sense to apply PCA to just one sample. So how can we manage these preprocessing techniques in the training phase in the test data, too?
Thanks in advance.