标题： 随机化算法用于克罗内克张量分解及其应用 显示英文标题

标题： Randomized algorithms for Kroncecker tensor decomposition and applications

摘要： 本文提出了用于计算Kronecker张量分解（KTD）的快速随机算法。 所提出的算法可以比现有的最先进算法更快地将给定的张量分解为KTD格式。 我们的主要思想是使用随机化框架显著降低计算复杂度。 我们进行了广泛的模拟，以验证所提出的随机算法的有效性和性能，与确定性算法相比，加速了几个数量级。 我们的模拟使用合成和真实世界的数据集，应用于张量补全、视频/图像压缩、图像去噪和图像超分辨率。 显示英文摘要

摘要： This paper proposes fast randomized algorithms for computing the Kronecker Tensor Decomposition (KTD). The proposed algorithms can decompose a given tensor into the KTD format much faster than the existing state-of-the-art algorithms. Our principal idea is to use the randomization framework to reduce computational complexity significantly. We provide extensive simulations to verify the effectiveness and performance of the proposed randomized algorithms with several orders of magnitude acceleration compared to the deterministic one. Our simulations use synthetics and real-world datasets with applications to tensor completion, video/image compression, image denoising, and image super-resolution