Nonnegative matrix factorization (NMF) is a powerful tool for data mining. However, the emergence of `big data' has severely challenged our ability to
compute this fundamental decomposition using deterministic algorithms...
paper presents a randomized hierarchical alternating least squares (HALS)
algorithm to compute the NMF. By deriving a smaller matrix from the nonnegative
input data, a more efficient nonnegative decomposition can be computed. Our
algorithm scales to big data applications while attaining a near-optimal
factorization. The proposed algorithm is evaluated using synthetic and real
world data and shows substantial speedups compared to deterministic HALS.