Informative Sensor and Feature Selection via Hierarchical Nonnegative Garrote

Abstract

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.

• Grouped variable
• Penalized regression
• Piecewise linear solution path
• Trajectory
• Variable selection.

ACKNOWLEDGMENTS

This work was funded in part by Ford Motor Company. The authors thank Ksenia Kozak and Nancy Wang of Ford for providing the motion trajectory data for the case study. The authors would also like to thank the editor, associate editor, and two anonymous referees for their helpful comments that have resulted in significant improvements in the article.