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arXiv 1311.6402eess.SYcs.SY
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在数据不确定性下稳健的最小二乘方法

Robust Least Squares Methods Under Bounded Data Uncertainties

Bilkent University(比尔肯特大学);University of Illinois at Urbana-Champaign(伊利诺伊大学厄巴纳-香槟分校)

N. Denizcan Vanli, Mehmet A. Donmez, Suleyman S. Kozat

AI总结:

本文研究在加性噪声下估计未知确定性信号的问题,提出一种最小化最坏情况后悔的稳健最小二乘方法,通过将问题转化为半定规划问题,展示其在结构化和正则化最小二乘问题中的应用。

摘要:

我们研究在加性噪声下估计未知确定性信号的问题,其中信号通过未知确定性数据矩阵观测。我们提出一种最小化最坏情况后悔的最小二乘优化框架,其中估计器具有不完美的数据矩阵和输出向量信息。我们定义估计器相对于最优最小二乘(LS)估计器的性能,该估计器针对未知数据矩阵和输出向量进行调优,定义为估计器的后悔。然后我们引入一种高效的稳健最小二乘估计方法,该方法最小化最坏情况下的后悔,不假设数据的结构。我们证明最小化这种最坏情况后悔可以转化为半定规划(SDP)问题。然后我们考虑正则化和结构化的最小二乘问题,并通过证明这些问题也可以转化为SDP问题,提出新的稳健估计方法。我们通过模拟展示了所提出算法相对于文献中已知替代方法的优势。

英文摘要:

We study the problem of estimating an unknown deterministic signal that is observed through an unknown deterministic data matrix under additive noise. In particular, we present a minimax optimization framework to the least squares problems, where the estimator has imperfect data matrix and output vector information. We define the performance of an estimator relative to the performance of the optimal least squares (LS) estimator tuned to the underlying unknown data matrix and output vector, which is defined as the regret of the estimator. We then introduce an efficient robust LS estimation approach that minimizes this regret for the worst possible data matrix and output vector, where we refrain from any structural assumptions on the data. We demonstrate that minimizing this worst-case regret can be cast as a semi-definite programming (SDP) problem. We then consider the regularized and structured LS problems and present novel robust estimation methods by demonstrating that these problems can also be cast as SDP problems. We illustrate the merits of the proposed algorithms with respect to the well-known alternatives in the literature through our simulations.

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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