Piecewise nonlinear regression with data augmentation

Piecewise regression represents a powerful tool to derive accurate yet modular models describing complex phenomena or physical systems. This paper presents an approach for learning PieceWise NonLinear (PWNL) functions in both supervised and semi-supervised setting. We further equip the proposed technique with method automatic generation of additional unsupervised data, which are leveraged improve overall accuracy estimate. The performance is preliminarily assessed on two simple simulation examples, where we show benefits using nonlinear local artificially generated data.