Efficient Nonnegative Matrix Factorization with Random Projections

The recent years have witnessed a surge of interests in Nonnegative Matrix Factorization (NMF) in data mining and machine learning fields. Despite its elegant theory and empirical success, one of the limitations of NMF based algorithms is that it needs to store the whole data matrix in the entire process, which requires expensive storage and computation costs when the data set is large and high-dimensional. In this paper, we propose to apply the random projection techniques to accelerate the NMF process. Both theoretical analysis and experimental validations will be presented to demonstrate the effectiveness of the proposed strategy.