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 o...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.展开更多
基金supported by National Natural Science Foundation of China (Grant No. 11571218)the State Key Program in the Major Research Plan of National Natural Science Foundation of China (Grant No. 91546202)+1 种基金Program for Changjiang Scholars and Innovative Research Team in Shanghai University of Finance and Economics (Grant No. IRT13077)Program for Innovative Research Team of Shanghai University of Finance and Economics
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.
