SEMI-SUPERVISED STOCHASTIC CONFIGURATION NETWORKS BASED ON MANIFOLD REGULARIZATION FRAMEWORK

Abstract

The stochastic configuration network (SCN) is an incremental neural network with fast convergence, efficient learning and strong generalization ability, and is widely used in fields such as medical data analysis. However, SCN is mainly used for supervised learning and its performance is limited in the case of scarce labeled data. To this end, this paper proposes semi-supervised SCN (MR SCN) in combination with manifold regularization to make full use of unla beled data to improve the model performance. Experimental results show that MR-SCN can still maintain high classification accuracy with a small number of labeled samples, which is better than LapRLS, SS-ELM and LapSVM, and the training time is shorter, showing good learning ability and computational efficiency