  We demonstrate a new deep learning autoencoder network, trained by a
nonnegativity constraint algorithm (NCAE), that learns features which show
part-based representation of data. The learning algorithm is based on
constraining negative weights. The performance of the algorithm is assessed
based on decomposing data into parts and its prediction performance is tested
on three standard image data sets and one text dataset. The results indicate
that the nonnegativity constraint forces the autoencoder to learn features that
amount to a part-based representation of data, while improving sparsity and
reconstruction quality in comparison with the traditional sparse autoencoder
and Nonnegative Matrix Factorization. It is also shown that this newly acquired
representation improves the prediction performance of a deep neural network.
