High-Dimensional Matched Subspace Detection When Data are Missing

被引：41

作者：

Balzano, Laura ^{[1
]}

Recht, Benjamin ^{[1
]}

Nowak, Robert ^{[1
]}

机构：

[1] Univ Wisconsin, Madison, WI 53706 USA

来源：

2010 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY | 2010年

关键词：

D O I：

10.1109/ISIT.2010.5513344

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

We consider the problem of deciding whether a highly incomplete signal lies within a given subspace. This problem, Matched Subspace Detection, is a classical, well-studied problem when the signal is completely observed. High-dimensional testing problems in which it may be prohibitive or impossible to obtain a complete observation motivate this work. The signal is represented as a vector in R-n, but we only observe m << n of its elements. We show that reliable detection is possible, under mild incoherence conditions, as long as m is slightly greater than the dimension of the subspace in question.

引用

页码：1638 / 1642

页数：5

共 11 条

[1]

Ardekani B., 1999, IEEE T MED IMAGING, V18

[2] Exact Matrix Completion via Convex Optimization [J].