Part-based data analysis with Masked Non-negative Matrix Factorization

We face the problem of interpreting parts of a dataset as small selections of features. Particularly, we propose a novel masked non- negative matrix factorization algorithm which is used to explain data as a composition of interpretable parts which are actually hidden in them and to introduce knowledge in the factorization process. Numerical ex- amples prove the effectiveness of the proposed MNMF algorithm as a useful tool for Intelligent Data Analysis.