Robust low-rank data matrix approximations

被引：8

作者：

Feng XingDong ^{[1
,2
]}

He XuMing ^{[3
]}

机构：

[1] Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai 200433, Peoples R China

[2] Minist Educ, Key Lab Math Econ SUFE, Shanghai 200433, Peoples R China

[3] Univ Michigan, Dept Stat, Ann Arbor, MI 48109 USA

来源：

SCIENCE CHINA-MATHEMATICS | 2017年 / 60卷 / 02期

基金：

中国国家自然科学基金; 美国国家科学基金会;

关键词：

dimension reduction; low-rank; M-estimator; regression; robust estimator; singular value decomposition; PRINCIPAL-COMPONENTS-ANALYSIS; HIGH DIMENSIONS; DECOMPOSITION; REGRESSION; SQUARES; PCA;

D O I：

10.1007/s11425-015-0484-1

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

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.

引用

页码：189 / 200

页数：12

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