用于岭回归的快速数据驱动贪心传感器选择
Fast Data-driven Greedy Sensor Selection for Ridge Regression
AI总结：
针对岭回归估计目标变量的传感器选择问题，提出一种数据驱动贪心算法，通过推导递推关系快速计算贪心解，设正正则化参数防过拟合，在人工及真实数据集上验证了其比现有算法更准确的估计性能。
摘要：
我们提出一种数据驱动的传感器选择算法，旨在从所选测量值中准确估计目标变量。假设目标变量由基于数据训练的岭回归估计器估计。所提算法通过贪心方式选择传感器，以最小化估计器的代价函数。通过设置正正则化参数，可实现防止所得估计器过拟合的传感器选择。我们推导了一些递推关系，利用这些关系能在极短时间内计算出贪心解。针对从线性系统生成的人工数据集以及用于选择压力传感器位置以估计地面车辆偏航角的真实世界数据集，验证了所提算法的有效性。数据集演示表明，在某些条件下，所提算法选择的传感器集比现有数据驱动选择算法产生更准确的估计。此外，当存在过拟合问题时，在该算法中设置正正则化参数可实现准确估计。
英文摘要：
We propose a data-driven sensor-selection algorithm for accurate estimation of the target variables from the selected measurements. The target variables are assumed to be estimated by a ridge-regression estimator which is trained based on the data. The proposed algorithm greedily selects sensors for minimizing the cost function of the estimator. Sensor selection which prevents overfitting of the resulting estimator can be realized by setting a positive regularization parameter. The greedy solution is computed in quite a short time by using some recurrent relations that we derive. The effectiveness of the proposed algorithm is verified for artificial datasets which are generated from linear systems and a real-wold dataset which are aimed for selection of pressure-sensor locations for estimating yaw angle of a ground vehicle. The demonstration for the datasets reveal that the proposed algorithm computes a sensor set resulting in more accurate estimation than existing data-drive selection algorithms in some conditions. Furthermore, it is confirmed that setting a positive regularization parameter in the proposed algorithm leads to accurate estimation when overfitting is problematic.
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。
