Efficient online learning of a non-negative sparse autoencoder
Andre Lemme, Felix Reinhart, Jochen J. Steil · 2010
Abstract. We introduce an efficient online learning mechanism for nonnegative sparse coding in autoencoder neural networks. In this paper we compare the novel method to the batch algorithm non-negative matrix factorization with and without sparseness constraint. We show that the efficient autoencoder yields to better sparseness and lower reconstruction errors than the batch algorithms on the MNIST benchmark dataset. 1