Restricted Boltzmann machines (RBMs) have been successfully applied in unsupervised learning and image density-based modeling. The aim of the pre-training step for RBMs is to discover an unknown stationary distri- bution based on the sample data that has the lowest energy. However, conventional RBM pre-training is sensitive to the initial weights and bias. The selection of initial values in RBM pre-training will directly affect the capabilities and
efficiency of the learning process. This paper uses princi- pal component analysis to capture the principal component directions of the training data. A set of initial parameter val- ues for the RBM can be obtained by computing the same reconstruction of the data. Experiments on the Yale and MNIST datasets show that the proposed method not only retains a strong learning ability, but also significantly accel- erates the learning speed.

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