Deep Belief Network for clustering and classification of a continuous data

Abstract

Deep Belief Network (DBN) is a deep architecture that consists of a stack of Restricted Boltzmann Machines (RBM). The deep architecture has the benefit that each layer learns more complex features than layers before it. DBN and RBM could be used as a feature extraction method also used as neural network with initially learned weights. The approach proposed depends on DBN in clustering and classification of continuous input data without using back propagation in the DBN architecture. DBN should have a better a performance than the traditional neural network due the initialization of the connecting weights rather than just using random weights in NN. Each layer in DBN (RBM) depends on Contrastive Divergence method for input reconstruction which increases the performance of the network.