Part-based data analysis with masked non-negative matrix factorization

Abstract

We face the problem of interpreting parts of a dataset as small selections of features. Particularly, we propose a novel masked nonnegative matrix factorization algorithm which is used either to explain data as a composition of interpretable parts (which are actually hidden in them) and to introduce knowledge in the factorization process. Numerical examples prove the effectiveness of the proposed algorithm as a useful tool for Intelligent Data Analysis. © 2014 Springer International Publishing.