Gaussian Fitting based Fda for Chemometrics

Author

Tuomas Kärnä
Amaury Lendasse, Missouri University of Science and TechnologyFollow

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 Spectremetric 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. © Springer-Verlag Berlin Heidelberg 2007.

Recommended Citation

T. Kärnä and A. Lendasse, "Gaussian Fitting based Fda for Chemometrics," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4507 LNCS, pp. 186 - 193, Springer, Jan 2007.

The definitive version is available at https://doi.org/10.1007/978-3-540-73007-1_23

Department(s)

Engineering Management and Systems Engineering

International Standard Book Number (ISBN)

978-354073006-4

International Standard Serial Number (ISSN)

1611-3349; 0302-9743

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Springer, All rights reserved.

Publication Date

01 Jan 2007