论文标题
使用增强的多种模式的张量完成，低级别的先验和总变化
Tensor completion using enhanced multiple modes low-rank prior and total variation
论文作者
论文摘要
在本文中，我们提出了一个新型模型，以同时对基础张量的全模式矩阵进行双核标准化的低级矩阵因子化来恢复低级数张量。应用块连续的上限最小化算法来解决该模型。可以建立我们的算法的子序列收敛，并且我们的算法在某些温和条件下会收敛于坐标的最小化器。关于三种类型的公共数据集的几项实验表明，我们的算法可以从样本中恢复各种低级别张量，而不是其他测试张量张量完成方法。
In this paper, we propose a novel model to recover a low-rank tensor by simultaneously performing double nuclear norm regularized low-rank matrix factorizations to the all-mode matricizations of the underlying tensor. An block successive upper-bound minimization algorithm is applied to solve the model. Subsequence convergence of our algorithm can be established, and our algorithm converges to the coordinate-wise minimizers in some mild conditions. Several experiments on three types of public data sets show that our algorithm can recover a variety of low-rank tensors from significantly fewer samples than the other testing tensor completion methods.
