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Informative Sensor and Feature Selection via Hierarchical Nonnegative Garrote

Summary: [This abstract is based on the authors' 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.

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  • Topics: Statistics
  • Keywords: Variable selection, Parameters, Data, Simulations, Optimization, Statistics, Results
  • Author: Paynabar, Kamran; Jin, Judy; Reed, Matthew P.;
  • Journal: Technometrics