Nonnegative autoencoder with simplified random neural network

Yonghua Yin, Erol Gelenbe

This paper proposes new nonnegative (shallow and multi-layer) autoencoders by combining the spiking Random Neural Network (RNN) model, the network architecture typical used in deep-learning area and the training technique inspired from nonnegative matrix factorization (NMF). The shallow autoencoder is a simplified RNN model, which is then stacked into a multi-layer architecture. The learning algorithm is based on the weight update rules in NMF, subject to the nonnegative probability constraints of the RNN. The autoencoders equipped with this learning algorithm are tested on typical image datasets including the MNIST, Yale face and CIFAR-10 datasets, and also using 16 real-world datasets from different areas. The results obtained through these tests yield the desired high learning and recognition accuracy. Also, numerical simulations of the stochastic spiking behavior of this RNN auto encoder, show that it can be implemented in a highly-distributed manner.

Knowledge Graph



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