论文标题
在矩阵和张量完成的特征等级上
On characteristic rank for matrix and tensor completion
论文作者
论文摘要
在本讲座中，我们讨论了一个基本概念，称为{\ it特征等级}，该概念提出了一个一般框架，用于表征信号处理中使用的各种低维模型的基本属性。在下面，我们使用两个示例说明了此框架：矩阵和三向张量的完成问题，考虑基本属性包括矩阵或张量的可识别性，给定部分观察。在本说明中，我们考虑没有观察噪声的情况来说明原理。
In this lecture note, we discuss a fundamental concept, referred to as the {\it characteristic rank}, which suggests a general framework for characterizing the basic properties of various low-dimensional models used in signal processing. Below, we illustrate this framework using two examples: matrix and three-way tensor completion problems, and consider basic properties include identifiability of a matrix or tensor, given partial observations. In this note, we consider cases without observation noise to illustrate the principle.
