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research-article

A backpropagation learning algorithm with graph regularization for feedforward neural networks

Published: 01 August 2022 Publication History

Abstract

The backpropagation (BP) neural network has been widely used in many fields. However, it is still a great challenge to design the architecture and obtain optimal parameters for BP neural networks. For improving the generalization performance, regularization is the most popular technique to train the BP neural networks. In this paper, we propose a novel BP algorithm with graph regularization (BPGR) to obtain optimal parameters, by imposing the graph regularization term to the error function. The essential idea is to force the latent features of hidden layer to be more concentrated, which enhances the generalization performance. Besides, the proposed modified graph regularization facilitates the calculation of gradient and is more capable to penalize the extreme values of weights. Furthermore, the graph regularization can also be integrated with deep neural networks to improve their generalization performance. In addition, we provide the convergence analysis of our method BPGR under some regularity conditions. By comparison on several datasets with five activation functions, experimental results validate the theoretical analysis and demonstrate outstanding performance of BPGR.

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        Published In

        cover image Information Sciences: an International Journal
        Information Sciences: an International Journal  Volume 607, Issue C
        Aug 2022
        1637 pages

        Publisher

        Elsevier Science Inc.

        United States

        Publication History

        Published: 01 August 2022

        Author Tags

        1. 00-01
        2. 99-00

        Author Tags

        1. Graph regularization
        2. Convergence
        3. Backpropagation
        4. Neural network

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