Title: Randomized algorithms for matrix decomposition

Matrix decompositions, and especially SVD for large matrices, are very important tools in data analysis. When big data is processed, the computation of matrix decompositions becomes expensive and impractical. In recent years, several algorithms, which approximate matrix decomposition, have been developed. These algorithms are based on metric conservation features for linear spaces of random projections. We present a randomized method based on sparse matrix distribution that achieves a fast approximation with bounded error for low-rank matrix decomposition

This work appears in "Matrix decompositions using sub-gaussian random matrices", Information and Inference: A Journal of the IMA, (2018).