High-Dimensional Matched Subspace Detection When Data are Missing
Information Theory
2011-01-25 v2 math.IT
Abstract
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.
Cite
@article{arxiv.1002.0852,
title = {High-Dimensional Matched Subspace Detection When Data are Missing},
author = {Laura Balzano and Bejamin Recht and Robert Nowak},
journal= {arXiv preprint arXiv:1002.0852},
year = {2011}
}