Iterative hard thresholding for low-rank recovery from rank-one projections
Iterative hard thresholding for low-rank recovery from rank-one projections
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DOI：
10.1016/j.laa.2019.03.007
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发表时间：
2018-10
影响因子：
1.1
通讯作者：
S. Foucart;Srinivas Subramanian
中科院分区：
文献类型：
--
作者：
S. Foucart;Srinivas Subramanian
提出并分析了一种用于恢复通过压缩线性测量获得的低秩矩阵的新算法。该算法是用于低秩恢复的迭代硬阈值算法的变体，旨在在标准秩限制等距属性失败的情况下取得成功，例如在次指数非结构化测量或次高斯一阶测量的情况下。该算法的稳定性和鲁棒性是基于独特的矩阵分析成分而建立的，并且其性能得到了数值验证。
A novel algorithm for the recovery of low-rank matrices acquired via compressive linear measurements is proposed and analyzed. The algorithm, a variation on the iterative hard thresholding algorithm for low-rank recovery, is designed to succeed in situations where the standard rank-restricted isometry property fails, e.g. in case of subexponential unstructured measurements or of subgaussian rank-one measurements. The stability and robustness of the algorithm are established based on distinctive matrix-analytic ingredients and its performance is substantiated numerically.