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Gaussian basis functions for chemometrics
Author(s) -
Kärnä Tuomas,
Corona Francesco,
Lendasse Amaury
Publication year - 2008
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.1166
Subject(s) - dimensionality reduction , chemometrics , curse of dimensionality , dimension (graph theory) , computer science , basis (linear algebra) , support vector machine , gaussian , artificial intelligence , basis function , field (mathematics) , algorithm , pattern recognition (psychology) , mathematics , machine learning , mathematical analysis , physics , geometry , quantum mechanics , pure mathematics
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 optimised 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. To utilise 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.