  Restricted Boltzmann Machine (RBM) is an importan- t generative model
modeling vectorial data. While applying an RBM in practice to images, the data
have to be vec- torized. This results in high-dimensional data and valu- able
spatial information has got lost in vectorization. In this paper, a
Matrix-Variate Restricted Boltzmann Machine (MVRBM) model is proposed by
generalizing the classic RBM to explicitly model matrix data. In the new RBM
model, both input and hidden variables are in matrix forms which are connected
by bilinear transforms. The MVRBM has much less model parameters, resulting in
a faster train- ing algorithm while retaining comparable performance as the
classic RBM. The advantages of the MVRBM have been demonstrated on two
real-world applications: Image super- resolution and handwritten digit
recognition.
