Abstract
High-Dimensional Data Are Becoming More and More Common, especially in the Field of Chemometrics. Nevertheless, it is Generally Known that Most of the Commonly Used Prediction Models Suffer from Curse of Dimensionality that is the Prediction Performance Degrades as Data Dimension Grows. Therefore, It is Important to Develop Methodology for Reliable Dimensionality Reduction. in This Paper, We Propose a Method that is based on Functional Approximation using Gaussian Basis Functions. the Basis Functions Are Optimized to Accurately Fit the Spectral Data using Nonlinear Gauss-Newton Algorithm. the Fitting Weights Are Then Used as Training Data to Build a Least-Squares Support Vector Machine (LS-SVM) Model. Toutilise the Reduced Data Dimension, Relevant Variables Are Further Selected using Forward-Backward (Fb) Selection. the Methodology is Experimented with Three Datasets Originating from the Food Industry. the Results Show that the Proposed Method Can Be Used for Dimensionality Reduction Without Loss of Precision. Copyright © 2008 John Wiley & Sons, Ltd.
Recommended Citation
T. Kärnä et al., "Gaussian Basis Functions for Chemometrics," Journal of Chemometrics, vol. 22, no. 11 thru 12, pp. 701 - 707, Wiley, Jan 2008.
The definitive version is available at https://doi.org/10.1002/cem.1166
Department(s)
Engineering Management and Systems Engineering
Publication Status
Full Access
Keywords and Phrases
Dimensionality reduction; LS-SVM; Nonlinearity; Regression; Variable selection
International Standard Serial Number (ISSN)
1099-128X; 0886-9383
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Wiley, All rights reserved.
Publication Date
01 Jan 2008
