  Nonnegative Matrix Factorization (NMF) is a widely used technique for data
representation. Inspired by the expressive power of deep learning, several NMF
variants equipped with deep architectures have been proposed. However, these
methods mostly use the only nonnegativity while ignoring task-specific features
of data. In this paper, we propose a novel deep approximately orthogonal
nonnegative matrix factorization method where both nonnegativity and
orthogonality are imposed with the aim to perform a hierarchical clustering by
using different level of abstractions of data. Experiment on two face image
datasets showed that the proposed method achieved better clustering performance
than other deep matrix factorization methods and state-of-the-art single layer
NMF variants.
