Randomized block Krylov method for approximation of truncated tensor SVD
This work addresses tensor decomposition for data completion and compression, but it appears incremental as it applies an existing randomized method to a specific tensor problem.
The paper tackles the problem of approximating truncated tensor SVD using a randomized block Krylov method, with experimental results showing promising efficiency and feasibility on synthetic and real-world data.
This paper is devoted to studying the application of the block Krylov subspace method for approximation of the truncated tensor SVD (T-SVD). The theoretical results of the proposed randomized approach are presented. Several experimental experiments using synthetics and real-world data are conducted to verify the efficiency and feasibility of the proposed randomized approach, and the numerical results show that the proposed method provides promising results. Applications of the proposed approach to data completion and data compression are presented.