Multi-Channel missing data recovery by exploiting the low-rank hankel structures
Multi-Channel missing data recovery by exploiting the low-rank hankel structures
复制标题
利用低秩hankel结构进行多通道丢失数据恢复
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
J. Chow
中科院分区：
文献类型：
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作者：
Shuai Zhang;Yingshuai Hao;Meng Wang;J. Chow
本文利用数据中的时间相关性，研究了低阶矩阵补全问题。将低阶矩阵与电力系统等动力系统相结合，提出了一种新的模型，称为多通道低阶Hankel矩阵，用于刻画时间序列集合中固有的低维结构。提出了一种具有线性收敛速度的加速多通道快速迭代硬阈值算法(AM-FIHT)来恢复缺失点。与传统的低阶完成方法相比，成功恢复所需的观察条目数显著减少。对记录的PMU数据进行了数值实验，验证了该方法的有效性。
This paper studies the low-rank matrix completion problem by exploiting the temporal correlations in the data. Connecting low-rank matrices with dynamical systems such as power systems, we propose a new model, termed multi-channel low-rank Hankel matrices, to characterize the intrinsic low-dimensional structures in a collection of time series. An accelerated multi-channel fast iterative hard thresholding (AM-FIHT) with a linear convergence rate is proposed to recover the missing points. The required number of observed entries for successful recovery is significantly reduced from conventional low-rank completion methods. Numerical experiments are carried out on recorded PMU data to verify the proposed method.