  The main aim of this paper is to develop a new algorithm for computing
nonnegative low rank tensor approximation for nonnegative tensors that arise in
many multi-dimensional imaging applications. Nonnegativity is one of the
important property as each pixel value refers to nonzero light intensity in
image data acquisition. Our approach is different from classical nonnegative
tensor factorization (NTF) which requires each factorized matrix and/or tensor
to be nonnegative. In this paper, we determine a nonnegative low Tucker rank
tensor to approximate a given nonnegative tensor. We propose an alternating
projections algorithm for computing such nonnegative low rank tensor
approximation, which is referred to as NLRT. The convergence of the proposed
manifold projection method is established. Experimental results for synthetic
data and multi-dimensional images are presented to demonstrate the performance
of NLRT is better than state-of-the-art NTF methods.
