We introduce an efficient online learning mechanism for non- negative 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.
['Andre Lemme', 'R. Felix Reinhart', 'Jochen J. Steil']
Efficient online learning of a non-negative sparse autoencoder