Journal article
Variable selection of high-dimensional non-parametric nonlinear systems by derivative averaging to avoid the curse of dimensionality
Automatica (Oxford), Vol.101, pp.138-149
03/2019
DOI: 10.1016/j.automatica.2018.11.019
Abstract
In identification, variable selection for a nonlinear non-parametric and high-dimensional system is the first and often one of the most difficult problems. The issue is the curse of dimensionality. This paper presents a numerically efficient algorithm of variable selection for high-dimensional nonlinear non-parametric systems. It is based on averaging derivatives and relies on one dimensional estimates of the density function and its derivative. Thus, it avoids the curse of dimensionality usually encountered for high-dimensional systems. Theoretical analysis is provided and the conditions are derived for a variable to contribute or not to contribute for a large class of nonlinear systems. Further, convergence results are established.
Details
- Title: Subtitle
- Variable selection of high-dimensional non-parametric nonlinear systems by derivative averaging to avoid the curse of dimensionality
- Creators
- Er-Wei Bai - University of IowaChangming Cheng - Shanghai Jiao Tong UniversityWen-Xiao Zhao - Academy of Mathematics and Systems Science
- Resource Type
- Journal article
- Publication Details
- Automatica (Oxford), Vol.101, pp.138-149
- Publisher
- Elsevier Ltd
- DOI
- 10.1016/j.automatica.2018.11.019
- ISSN
- 0005-1098
- eISSN
- 1873-2836
- Grant note
- 2016YFB0901900 / National Key Research and Development Program of China 2014CB845301 / National Key Basic Research Program of China 61822312; 61573345 / National Natural Science Foundation (NSF) of China (http://dx.doi.org/10.13039/501100001809) CNS-1239509 / National Science Foundation
- Language
- English
- Date published
- 03/2019
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984197176702771
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