  Nonnegative Matrix Factorization(NMF) is a common used technique in machine
learning to extract features out of data such as text documents and images
thanks to its natural clustering properties. In particular, it is popular in
image processing since it can decompose several pictures and recognize common
parts if they're located in the same position over the photos. This paper's aim
is to present a way to add the translation invariance to the classical NMF,
that is, the algorithms presented are able to detect common features, even when
they're shifted, in different original images.
