Abstract:   To improve the restricted Boltzmann machine (RBM)’s data generalization ability and resolve the features homogenization problem, a random RBM group (RRBMG) design is proposed. The dimensions of observation data were randomly divided into groups, and the childRBMs were built based on the combined data group. Two methods based on the structural stories were used to compose hidden units’ layer finally, shallow structure by mean output, and deep structure through the formation of highlevel hidden units’ layer. The theoretical analysis shows that, with the increase of models’ number in the group, the training objectives of RRBMG will gradually approach the training objectives of standard RBM, and can effectively reduce the impact of feature homogeneity. The experimental results show that, compared with dropout algorithm, the proposed RRBMG can effectively improve the feature learning ability of RBM, and use the shallow structure and deep structure features to increase the classification accuracy of mixed national institute of standards and technology (MNIST) database experiment by 2% and 0.4%.

Key words:  , machine learning, deep learning, restricted Boltzmann machine (RBM), deep Boltzmann machine