A new deep belief network based on RBM with glial chains

Abstract

A deep belief network (DBN) is an unsupervised learning method that is widely used to build hierarchical structures and learn feature representations based on unlabeled data; however, it is limited in feature acquisition because no interconnections occur between neurons in the same layer. Moreover, information shared between different neurons may be ignored, so deep correlative features of the data cannot be recognized. Inspired by the functions of glial cells in the neural network of the human brain, this paper proposes a variation of DBN based on Restricted Boltzmann Machines (RBMs) with glial chains. Additionally, an improved greedy layer-wise learning algorithm for the new DBN is applied to enhance learning accuracy and extract more information from the data. Furthermore, to ensure the glial effect is maintained, the glial chain is always contained in the supervised fine-tuning process. The experimental results based on benchmark image datasets show that the proposed DBN model can acquire more abstract features and achieve a lower mean squared error (MSE) than traditional DBN and other learning algorithms.