  Interpretability has become an important issue in the machine learning field,
along with the success of layered neural networks in various practical tasks.
Since a trained layered neural network consists of a complex nonlinear
relationship between large number of parameters, we failed to understand how
they could achieve input-output mappings with a given data set. In this paper,
we propose the non-negative task decomposition method, which applies
non-negative matrix factorization to a trained layered neural network. This
enables us to decompose the inference mechanism of a trained layered neural
network into multiple principal tasks of input-output mapping, and reveal the
roles of hidden units in terms of their contribution to each principal task.
