The existing randomized algorithms need an initial estimation of the tubal rank to compute a tensor singular value decomposition. This paper proposes a new randomized fixed-precision algorithm which for a given third-order tensor and a prescribed approximation error bound, it automatically finds the tubal rank, and corresponding low tubal rank approximation. The algorithm is based on the random projection technique and equipped with the power iteration method for achieving a better accuracy. We conduct simulations on synthetic and real-world datasets to show the efficiency and performance of the proposed algorithm.

翻译：现有的随机算法需要对管状等级进行初步估计, 以计算单值的分解。 本文提出一种新的随机固定精度算法, 对于特定的第三阶拉和规定的近似差错, 它会自动发现管状和相应的低管级近差。 该算法基于随机投影技术, 并配有更精确地实现更精确性能的迭代法。 我们在合成和真实世界的数据集上进行模拟, 以显示提议的算法的效率和性能 。