Informative Sensor and Feature Selection via Hierarchical Nonnegative Garrote
Informative Sensor and Feature Selection via Hierarchical Nonnegative Garrote
复制标题
通过分层非负绞索进行信息传感器和特征选择
DOI：
10.1080/00401706.2014.947383
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发表时间：
2015
期刊：
影响因子：
2.5
通讯作者：
M. Reed
中科院分区：
文献类型：
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作者：
K. Paynabar;Judy Jin;M. Reed
在过程的每个站或系统的每个元件中放置传感器来监控其状态或性能通常过于昂贵或在物理上是不可能的。因此，需要一种系统的方法来选择重要的传感变量。该方法不仅应该能够识别来自分布式传感系统的多流信号中的重要传感器/信号，而且还应该能够从所选信号的高维向量中提取一小组可解释的特征。为此，我们开发了一种新的分层正则化方法，称为分层非负绞环（NNG）。在层次结构的第一级，使用一组 NNG 来选择重要信号，在第二级，使用具有良好估计系数特性的 NNG 修改版本来选择每个信号内的各个特征。通过蒙特卡洛模拟评估所提出方法的性能并与其他现有方法进行比较。通过案例研究来演示所提出的方法，该方法可用于开发预测模型，以根据测试的驾驶员的运动轨迹信号评估车辆设计舒适度。本文有在线补充材料。
Placing sensors in every station of a process or every element of a system to monistor its state or performance is usually too expensive or physically impossible. Therefore, a systematic method is needed to select important sensing variables. The method should not only be capable of identifying important sensors/signals among multistream signals from a distributed sensing system, but should also be able to extract a small set of interpretable features from the high-dimensional vector of a selected signal. For this purpose, we develop a new hierarchical regularization approach called hierarchical nonnegative garrote (NNG). At the first level of hierarchy, a group NNG is used to select important signals, and at the second level, the individual features within each signal are selected using a modified version of NNG that possesses good properties for the estimated coefficients. Performance of the proposed method is evaluated and compared with other existing methods through Monte Carlo simulation. A case study is conducted to demonstrate the proposed methodology that can be applied to develop a predictive model for the assessment of vehicle design comfort based on the tested drivers’ motion trajectory signals. This article has supplementary material online.
DOI：
--
发表时间：
2006
期刊：
--
影响因子：
--
作者：
H. Bondell;B. Reich
通讯作者：
H. Bondell;B. Reich