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—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.

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