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Gaussian Fitting Based FDA for Chemometrics

  • Tuomas Kärnä
  • Amaury Lendasse
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4507)

Abstract

In Functional Data Analysis (FDA) multivariate data are considered as sampled functions. We propose a non-supervised method for finding a good function basis that is built on the data set. The basis consists of a set of Gaussian kernels that are optimized for an accurate fitting. The proposed methodology is experimented with two spectrometric data sets. The obtained weights are further scaled using a Delta Test (DT) to improve the prediction performance. Least Squares Support Vector Machine (LS-SVM) model is used for estimation.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Tuomas Kärnä
    • 1
  • Amaury Lendasse
    • 1
  1. 1.Helsinki University of Technology, Laboratory of Computer and Information Science, P.O. Box 5400 FI-02015Finland

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