An Approximation Algorithm for Third-order Tensor Low-tubal Rank Approximation Based on Block Krylov Iteration
A new tensor low-tubal rank approximation algorithm is proposed,which is based on block Krylov iteration and random count embedding matrix techniques.By using block Krylov iteration and random count embedding matrix techniques,the accuracy of the algorithm is guaranteed at a high level.Compared with other prevailing algorithms,the experimental results of color images show that the proposed algorithm has higher PSNR values and less computing time.The synthetic data experiments show that the proposed algorithm is superior to prevailing methods in terms of projection error and relative error.
third-order tensortensor singular value decompositiontubal rankblock Krylov iterationrandom count embedding
NETL
NSTL
万方数据
