Data mining using NMF and generalized matrix inverse

Non-negative matrix factorization is an important method helpful in the analysis of high dimensional datasets. It has a number of applications including pattern recognition, data clustering, information retrieval or computer security. One its significant drawback lies in its computational complexity. In this paper, we introduce a new method allowing fast approximate transformation from input space to feature space defined by non-negative matrix factorization and discuss some examples of its application.