Row-Action Projections for Nonnegative Matrix Factorization

Nonnegative Matrix Factorization (NMF) is more and more frequently used for analyzing large-scale nonnegative data, where the number of samples and/or the number of observed variables is large. In the paper, we discuss two applications of the row-action projections in the context of learning latent factors from large-scale data. First, we show that they can be efficiently used for improving the on-line learning in dynamic NMF. Next, they can also considerably reduce the computational complexity of the optimization algorithms used for factor learning from strongly redundant data. The experiments demonstrate high efficiency of the proposed methods.