i am using scikit learn's RBM implementation. There are two problems: 1.
The running time is O(d^2) where d is the number of features. This becomes a problem in using high dimensionality sparse features. Consider features that come from feature hashing for instance. 2. It only allows for binary visible features. Do I have to change the sklearn code to have non binary units or there is some trick that I am unaware of? I am expecting RBMs with 4 features to have a better fit than a mixture of 2 gaussians (that has a similar number of parameters). Has anyone seen any experiments done on RBMs for unsupervised modeling other than pretraining?
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