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arXiv 1312.0641cs.ITmath.ITmath.OCmath.STstat.TH
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具有精确边信息的带噪声线性反问题的简单界

Simple Bounds for Noisy Linear Inverse Problems with Exact Side Information

Samet Oymak, Christos Thrampoulidis, Babak Hassibi

摘要:

本文考虑线性逆问题,我们希望估计一个结构化的信号$x$从其损坏的意见。当问题是不适定的,它是自然的,利用凸函数$f(\cdot)$,利用信号的结构。例如,$\ell_1$范数可用于稀疏信号。为了进行估计,我们考虑两个著名的凸规划:1)二阶锥规划(SOCP),和2)Lasso。假设高斯测量,我们表明,如果精确的信息值$f(x)$或$\ell_2$ -规范的噪声是可用的,可以做一个特别好的工作估计。特别地,重构误差变得与信号的“稀疏性”成比例,而不是与噪声向量的环境维度成比例。我们将我们的结果连接到现有的作品,并提供了一个讨论我们的结果与标准的最小二乘问题的关系。我们的误差界是非渐近的和尖锐的,他们适用于任意凸函数,不假设任何分布的噪声。

英文摘要:

This paper considers the linear inverse problem where we wish to estimate a structured signal $x$ from its corrupted observations. When the problem is ill-posed, it is natural to make use of a convex function $f(\cdot)$ that exploits the structure of the signal. For example, $\ell_1$ norm can be used for sparse signals. To carry out the estimation, we consider two well-known convex programs: 1) Second order cone program (SOCP), and, 2) Lasso. Assuming Gaussian measurements, we show that, if precise information about the value $f(x)$ or the $\ell_2$-norm of the noise is available, one can do a particularly good job at estimation. In particular, the reconstruction error becomes proportional to the "sparsity" of the signal rather than the ambient dimension of the noise vector. We connect our results to existing works and provide a discussion on the relation of our results to the standard least-squares problem. Our error bounds are non-asymptotic and sharp, they apply to arbitrary convex functions and do not assume any distribution on the noise.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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