Matrix Variate RBM Model with Gaussian Distributions

Restricted Boltzmann Machine (RBM) is a particular type of random neural network models modeling vector data based on the assumption of Bernoulli distribution. For multidimensional 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 these issues, 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 algorithms, MVGRBM can model real value data better and it has good performance in image classification. To prove that adding Gaussian parameters could model input data well, we compared the reconstruction performance of the Gaussian parameters updating and fixed.