Multiple Nonnegative Matrices Factorization (MNMF) is a promising method to study and analyze a dataset which has different types of features or relationships. However, due to the high computational cost, MNMF cannot meet the needs of time response for large-scale datasets. In this paper, we introduce a Parallel Multiple Nonnegative Matrices Factorization (PMNMF) approach which is implemented on Graphics Processing Unit (GPU) under the Compute Unified Device Architecture (CUDA) framework.

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... mental studies demonstrate that PMNMF approach using GPU is able to obtain 100× speedup in comparison to the traditional multiple nonnegative matrices factorization under our experimental condition.