I have read about auto-encoders and RBMs being used to perform non-linear PCA by forcing the hidden layers to learn a good representation of the input features with reduced dimensions. But how do these networks actually find the principal components with the maximum variance which is what PCA is supposed to do?

up vote 2 down vote accepted

The RBM and auto-encoders algorithms do not reduce dimension in a way that maximizes the variance. They don't have such a property.

Your Answer

 
discard

By clicking "Post Your Answer", you acknowledge that you have read our updated terms of service, privacy policy and cookie policy, and that your continued use of the website is subject to these policies.

Not the answer you're looking for? Browse other questions tagged or ask your own question.