Big Data Matrix Singular Value Decomposition Based on Low-Rank Tensor Train Decomposition

This work proposes singular value decomposition (SVD) algorithms for very large-scale matrices based on a low-rank tensor decomposition technique called the tensor train (TT) format and shows that the computational complexity of the proposed algorithms scales logarithmically with the matrix size if the TT-ranks are bounded.