CUR+NMF for learning spectral features from large data matrix

Nonnegative matrix factorization (NMF) is a popular method for multivariate analysis of nonnegative data. It was successfully applied to learn spectral features from EEG data. However, the size of a data matrix grows, NMF suffers from ‘out of memory’ problem. In this paper we present a memory-reduced method where we downsize the data matrix using CUR decomposition before NMF is applied. Experimental results with two EEG data sets in BCI competition, confirm the useful behavior of the proposed method.