I have a large dataset (tens of thousands of predictors) on which I would like to perform feature reduction with the intent of better model-building for prediction. Deep Belief Networks seem to address the problem in such a way that they produce an output that bears some resemblance to that of PCA, i.e. a series of 'virtual' dimensions that can be used to explain variation in the data.
Unlike PCA however, it seems that every example of DBNs follow the original example of Hinton, being used to generate a series of starting weights for a feed-forward neural network. Is there a reason that nobody appears to use stacked RBMs as, say, input for linear regression or k-nearest neighbor?