The low multilinear rank approximation, also known as the truncated Tucker decomposition, has been extensively utilized in many applications that involve higher-order tensors. Popular methods for approximation usually rely directly on matrix SVD, therefore often suffer from notorious intermediate data explosion issue and are not easy to parallelize, especially when input tensor is large. In thi...