基于HOSVD的加权张量完成算法
Zehan Chao1, Longxiu Huang1, Deanna Needell1
1Department of Mathematics, University of California, Los Angeles, CA 90095, USA.
Journal of imaging
|July 31, 2024
概括
本研究介绍了一种高效的加权高阶单数值分解 (HOSVD) 算法,用于张量完成,有效地从不完整的数据中恢复低级张量. 该方法在模拟中证明了准确性和效率,推进了数据归算技术.
科学领域:
- 数据科学数据科学数据科学
- 应用数学 应用数学 应用数学
- 数字分析 数字分析
背景情况:
- 矩阵完成解决了低维结构矩阵中缺失的数据.
- 张量完成将这些原理扩展到更高阶的张量,旨在根据低级假设计算缺失的条目.
研究的目的:
- 开发一个高效的算法,用确定性和潜在的不统一的采样模式来完成张量.
- 为拟议的张量完成方法推导出误差极限.
主要方法:
- 提出了一种高效的加权高序单数值分解 (HOSVD) 算法.
- 使用加权度量来计算噪音张量观测的衍生误差边界.
主要成果:
- 权重的HOSVD算法证明了底层低级张量器的高效和准确的恢复.
- 在特定条件下,为拟议的方法建立了错误极限.
结论:
- 开发的加权HOSVD算法对于张量完成是有效的,即使是复杂的采样模式.
- 数字模拟证实了算法在合成和真实数据集上的效率和准确性.
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