Enhancing performance of the back-propagation algorithm based on a novel regularization method of preserving inter-object-distance of data

Abstract

Artificial neural networks, consisting of many levels of nonlinearities, have been widely used to deal with various supervised learning tasks. At present, the most popular and effective training method is back-propagation algorithm (BP). Inspired by manifold regularization framework, we introduce a novel regularization framework, which aims at preserving the inter-object-distance of the data. Then a refined BP algorithm (IOD-BP) is proposed by imposing the proposed regularization framework into the objective function of BP algorithm. Comparative experiments on various benchmark classification tasks show that the new regularization BP method significantly improves the performance of BP algorithm in terms of classification accuracy.