I have a
300D training data set and I want to use
autoencoders to reduce the dimensionality before running a machine learning model on this data set.
In the classical dimensionality reduction technique
PCA it is recommended that
PCA is run only on the training set, and testing/validation sets are "predicted" with it.
How is the procedure for
autoencoders? For example, I want to reduce the dimension of my training data from
10D, which means 10 neurons in the hidden layer of a
neural network (
autoencoders). My question is, should I run
autoencoders on the training/validation/testing sets separately?