Neural networks with marginalized corrupted hidden layer

Abstract

Overfitting is an important problem in neural networks (NNs) training. When the number of samples in the training set is limited, explicitly extending the training set with artificially generated samples is an effective solution. However, this method has the problem of high computational costs. In this paper we propose a new learning scheme to train single-hidden layer feedforward neural networks (SLFNs) with implicitly extended training set. The training set is extended by corrupting the hidden layer outputs of training samples with noise from exponential family distribution. When the number of corruption approaches infinity, in objective function explicitly generated samples can be expressed as the form of expectation. Our method, called marginalized corrupted hidden layer (MCHL), trains SLFNs by minimizing the loss function expected values under the corrupting distribution. In this way MCHL is trained with infinite samples. Experimental results on multiple data sets show that MCHL can be trained efficiently, and generalizes better to test data.