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Exploratory data analysis techniques to determine the dimensionality of complex nonlinear phenomena: The L-to-H transition at JET as a case study

Date

Authors

Murari, A

Mazon, D

Martin, Nigel

Vagliasindi, G

Gelfusa, M

Publisher

Institute of Electrical and Electronics Engineers (IEEE Inc)

Abstract

A strategy to identify and select the most relevant variables to study problems in the exact sciences, when large databases of data have to be explored, is formulated. It consists of a first exploratory stage, performed mainly with the classification and

Keywords

Keywords: Autoassociative neural networks; Classification and regression tree; Dimensionality reduction; Exploratory data analysis; Joint European Torus; L-to-H transition; Large database; Linear correlation; Non-linear correlations; Non-linear phenomena; PCA; Theo Autoassociative neural networks; dimensionality reduction; L-to-H transition; PCA

URI

http://hdl.handle.net/1885/78095

Collections

ANU Research Publications

Source

IEEE Transactions on Plasma Science

Type

Journal article

DOI

10.1109/TPS.2012.2187682

Restricted until

2037-12-31

Downloads

File

Description

01_Murari_Exploratory_data_analysis_2012.pdf (992.77 KB)

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