Incremental extreme learning machine based on deep feature embedded

Abstract

Extreme learning machine (ELM) algorithm is used to train Single-hidden Layer Feed forward Neural Networks. And Deep Belief Network (DBN) is based on Restricted Boltzmann Machine (RBM). The conventional DBN algorithm has some insufficiencies, i.e., Contrastive Divergence (CD) Algorithm is not an ideal approximation method to Maximum Likelihood Estimation. And bad parameters selected in RBM algorithm will produce a bad initialization in DBN model so that we will spend more training time and get a low classification accuracy. To solve the problems above, we summarize the features of extreme learning machine and deep belief networks, and then propose Incremental extreme learning machine based on Deep Feature Embedded algorithm which combines the deep feature extracting ability of Deep Learning Networks with the feature mapping ability of extreme learning machine. Firstly, we introduce Manifold Regularization to our model to attenuate the complexity of probability distribution. Secondly, we introduce the semi-restricted Boltzmann machine (SRBM) to our algorithm, and build a deep belief network based on SRBM. Thirdly, we introduce the thought of incremental feature mapping in ELM to the classifier of DBN model. Finally, we show validity of the algorithm by experiments.