论文标题
注意：基于Riemannian优化
Note: low-rank tensor train completion with side information based on Riemannian optimization
论文作者
论文摘要
当以包含模式-K $纤维跨度的子空间的形式提供其他侧面信息时，我们会考虑低级张量的训练列车完成问题。我们提出了一种基于Riemannian优化的算法来解决该问题。数值实验表明，与标准张量训练训练方法相比，所提出的算法需要更少的已知条目才能恢复张量。
We consider the low-rank tensor train completion problem when additional side information is available in the form of subspaces that contain the mode-$k$ fiber spans. We propose an algorithm based on Riemannian optimization to solve the problem. Numerical experiments show that the proposed algorithm requires far fewer known entries to recover the tensor compared to standard tensor train completion methods.
