Feature Extraction in Signal Regression: A Boosting Technique for Functional Data Regression

Abstract

The main objectives of feature extraction in signal regression are improved accuracy of the prediction of future data and the identification of relevant parts of the signal. A feature extraction procedure is introduced that uses boosting techniques to select the relevant parts of the signal, whereby the proposed blockwise boosting procedure simultaneously selects intervals in the signal’s domain and estimates the effect on the response. The blocks that are defined explicitly use the underlying metric of the signal. It is demonstrated in simulation studies and with real-world data that the proposed approach competes well with procedures like PLS, P-Spline signal regression, and functional data regression. Supplemental materials are available online.