Is this process correct?
Suppose We have a bunch of data such as MNIST.
We just feed all these data(without label) to RBM and resample each data from trained model.
Then output can be treated as new data for classification.
Do I understand it correctly? What is the purpose of using RBM?
You are correct, RBMs are a form of unsupervised learning algorithm that are commonly used to reduce the dimensionality of your feature space. Another common approach is to use autoencoders.
RBMs are trained using the contrastive divergence algorithm. The best overview of this algorithm comes from Geoffrey Hinton who came up with it. https://www.cs.toronto.edu/~hinton/absps/guideTR.pdf
A great paper about how unsupervised learning improves performance can be found at http://jmlr.org/papers/volume11/erhan10a/erhan10a.pdf. The paper shows that unsupervised learning provides better generalization and filters (if using CRBMs)
