Permutation NMF
This addresses a limitation in image processing for applications like feature detection, though it appears incremental as it builds on classical NMF.
The paper tackles the problem of making Nonnegative Matrix Factorization (NMF) translation-invariant to detect common features in images even when they are shifted, presenting algorithms that achieve this capability.
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.