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General Information
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2018 Vol.8(3): 191-197 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2018.8.3.686

An Improvement of the Nonlinear Semi-NMF Based Method by Considering Bias Vectors and Regularization for Deep Neural Networks

Ryosuke Arai, Akira Imakura, and Tetsuya Sakurai
Abstract—Backpropagation (BP) has been widely used as a de-facto standard algorithm to compute weights for deep neural networks (DNNs). The BP method is based on a stochastic gradient descent method using the derivatives of an objective function. As another approach, an alternating optimization method using linear and nonlinear semi-nonnegative matrix factorizations (semi-NMFs) has been proposed recently for computing weight matrices of fully-connected DNNs without bias vectors and regularization. In this paper, we proposed an improvement of the nonlinear semi-NMF based method by considering bias vectors and regularization. Experimental results indicate that the proposed method shows higher recognition performance than the nonlinear semi-NMF based method and competitive advantages to the conventional BP method.

Index Terms—Deep neural networks, nonlinear semi-nonnegative matrix factorization, regularization term.

The authors are with the Computer Science, University of Tsukuba, Tsukuba, Japan (e-mail: arai@mma.cs.tsukuba.ac.jp, imakura@cs.tsukuba.ac.jp, sakurai@cs.tsukuba.ac.jp).

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Cite: Ryosuke Arai, Akira Imakura, and Tetsuya Sakurai, "An Improvement of the Nonlinear Semi-NMF Based Method by Considering Bias Vectors and Regularization for Deep Neural Networks," International Journal of Machine Learning and Computing vol. 8, no. 3, pp. 191-197, 2018.

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