Improved Classification with Semi-supervised Deep Belief Network

Abstract

Abstract Classification problem is important for big data processing, and deep learning method named deep belief network (DBN) is successfully applied into classification. But traditional DBN is an unsupervised learning method, which leads to a gap between extracted features and concrete tasks. In this paper, a semi-supervised DBN (SSDBN) based on semi-supervised restricted Boltzmann machine (SSRBM) is proposed to shorten the gap and improve the accuracy of classification. Firstly, through introducing relevance constraint, supervised information is equivalently integrated into the learning process of restricted Boltzmann machine. Secondly, SSDBN-based model is constructed to improve the accuracy of classification problem. Finally, the proposed SSDBN is validated with hand-written digits classification standard dataset MNIST, and experimental results show that SSDBN outperforms traditional DBN and other models with respect to classification.