Robust low-rank data matrix approximations

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• 作者：XingDong Feng ; XuMing He
• 关键词：dimension reduction ; low ; rank ; M ; estimator ; regression ; robust estimator ; singular value decomposition
• 刊名：Science China Mathematics
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：60
• 期：2
• 页码：189-200
• 全文大小：
• 刊物类别：Mathematics and Statistics
• 刊物主题：Applications of Mathematics;
• 出版者：Science China Press
• ISSN：1869-1862
• 卷排序：60

文摘

We review some recent approaches to robust approximations of low-rank data matrices. We consider the problem of estimating a low-rank mean matrix when the data matrix is subject to measurement errors as well as gross outliers in some of its entries. The purpose of the paper is to make various algorithms accessible with an understanding of their abilities and limitations to perform robust low-rank matrix approximations in both low and high dimensional problems.