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The gap between the null space property and the restricted isometry property

机译:空空间属性和受限等距属性之间的差距

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The null space property (NSP) and the restricted isometry property (RIP) are two properties which have received considerable attention in the compressed sensing literature. As the name suggests, NSP is a property that depends solely on the null space of the measurement procedure and as such, any two matrices which have the same null space will have NSP if either one of them does. On the other hand, RIP is a property of the measurement procedure itself, and given an RIP matrix it is straightforward to construct another matrix with the same null space that is not RIP. We say a matrix is RIP-NSP if it has the same null space as an RIP matrix. We show that such matrices can provide robust recovery of compressible signals under Basis pursuit. More importantly, we constructively show that the RIP-NSP is stronger than NSP with the aid of this robust recovery result, which shows that RIP is fundamentally stronger than NSP. (C) 2016 Elsevier Inc. All rights reserved.
机译:零空间特性(NSP)和受限等距特性(RIP)是在压缩传感文献中受到相当多关注的两个特性。顾名思义,NSP是仅取决于测量过程的空白空间的属性,因此,如果两个空白中的任何一个具有相同的空白空间,则它们将具有NSP。另一方面,RIP是测量过程本身的属性,并且给定一个RIP矩阵,可以很容易地构造另一个具有相同非RIP空空间的矩阵。如果矩阵具有与RIP矩阵相同的空空间,则我们说该矩阵为RIP-NSP。我们表明,这种矩阵可以在基础追求下提供可压缩信号的鲁棒恢复。更重要的是,借助这一强大的恢复结果,我们有建设性地表明RIP-NSP比NSP强,这表明RIP从根本上比NSP强。 (C)2016 Elsevier Inc.保留所有权利。

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