On Computationally Tractable Selection of Experiments in Measurement-Constrained Regression Models
On Computationally Tractable Selection of Experiments in Measurement-Constrained Regression Models
复制标题
关于测量约束回归模型中实验的计算可处理选择
DOI：
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复制
发表时间：
2016
影响因子：
6
通讯作者：
Aarti Singh
中科院分区：
文献类型：
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作者：
Yining Wang;Adams Wei Yu;Aarti Singh
我们得出可计算方法的方法，从一个给定的设计点的大池中选择了一小部分实验设置。主要重点是线性回归模型，而技术也扩展到广义线性模型和Delta的方法（估计线性回归模型的函数）。该算法基于对原本棘手的组合优化问题的持续放松，并以采样或贪婪的程序作为后处理步骤。为算法建立了正式的近似保证，并且对合成和现实世界数据的数值结果证实了所提出方法的有效性。
We derive computationally tractable methods to select a small subset of experiment settings from a large pool of given design points. The primary focus is on linear regression models, while the technique extends to generalized linear models and Delta's method (estimating functions of linear regression models) as well. The algorithms are based on a continuous relaxation of an otherwise intractable combinatorial optimization problem, with sampling or greedy procedures as post-processing steps. Formal approximation guarantees are established for both algorithms, and numerical results on both synthetic and real-world data confirm the effectiveness of the proposed methods.