Informative Sensor and Feature Selection via Hierarchical Nonnegative Garrote

Placing sensors in every station of a process or every element of a system to monitor 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 multi-stream 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 non-negative 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 can be applied to develop a predictive model for the assessment of vehicle design comfort based on the tested drivers’ motion trajectory signals.