Finding a good initial configuration of parameters for restricted Boltzmann machine pre-training
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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 distribution 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 principal component analysis to capture the principal component directions of the training data. A set of initial parameter values 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 accelerates the learning speed.
KeywordsRBM PCA Pre-training Unsupervised learning
This work was supported by National Science Foundation of China under Grants 61375065, 61432014 and 61432012.
Compliance with ethical standards
Conflict of interest
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
Human and animal rights
This article does not contain any studies with human participants or animals performed by any of the authors.
Informed consent was obtained from all individual participants included in the study.
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