A fast optimized semi-supervised non-negative Matrix Factorization algorithm

Non-negative Matrix Factorization (NMF) is an unsupervised technique that projects data into lower dimensional spaces, effectively reducing the number of features of a dataset while retaining the basis information necessary to reconstruct the original data. In this paper we present a semi-supervised NMF approach that reduces the computational cost while improving the accuracy of NMF-based models. The advantages inherent to the proposed method are supported by the results obtained in two well-known face recognition benchmarks.