Classification model of restricted Boltzmann machine based on reconstruction error

Abstract

Many models are used to solve classification problems in machine learning. The classification restricted Boltzmann machine (ClassRBM) is a type of self-contained network model that is widely used in various classification applications. To implement classification, the ClassRBM updates the model parameters constantly during the training phase in terms of their class labels so that the model parameters learned from the ClassRBM are different from those learned from the conventional restricted Boltzmann machine (RBM), which is trained by unsupervised learning. In this paper, we demonstrate that the reconstruction errors of the ClassRBM are larger than those of the conventional RBM because of the label information. We then propose a classification model of the restricted Boltzmann machine based on these reconstruction errors. The reconstruction errors are used to train the proposed model to improve the classification performance of the ClassRBM. Extensive experiments are carried out to verify the proposed model. The experimental results demonstrate that the proposed model can improve the classification performance of the ClassRBM.