  Restricted Boltzmann Machine (RBM) is a particular type of random neural
network models modeling vector data based on the assumption of Bernoulli
distribution. For multi-dimensional and non-binary data, it is necessary to
vectorize and discretize the information in order to apply the conventional
RBM. It is well-known that vectorization would destroy internal structure of
data, and the binary units will limit the applying performance due to fickle
real data. To address the issue, this paper proposes a Matrix variate Gaussian
Restricted Boltzmann Machine (MVGRBM) model for matrix data whose entries
follow Gaussian distributions. Compared with some other RBM algorithm, MVGRBM
can model real value data better and it has good performance in image
classification.
