Generalized Matrix Learning Vector Quantizer for the Analysis of Spectral Data

Petra Schneider; Frank-Michael Schleif; Thomas Villmann; Michael Biehl

Generalized Matrix Learning Vector Quantizer for the Analysis of Spectral Data

Petra Schneider, Frank-Michael Schleif, Thomas Villmann, Michael Biehl

Intelligent Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

9 Citations (Scopus)

159 Downloads (Pure)

Abstract

The analysis of spectral data constitutes new challenges for machine learning algorithms due to the functional nature of the data. Special attention is paid to the metric used in the analysis. Recently, a prototype based algorithm has been proposed which allows the integration of a full adaptive matrix in the metric. In this contribution we study this approach with respect to band matrices and its use for the analysis of functional spectral data. The method is tested on data taken from food chemistry and satellite image data.

Original language	English
Title of host publication	Proc. European Symposium on Artificial Neural Networks
Subtitle of host publication	ESANN 2008
Editors	Michel Verleysen
Publisher	d-side publishing
Pages	451-456
Number of pages	6
Publication status	Published - 2008

Keywords

spectral data
band matrices
GMLVQ
metric adaptation
Learning Vector Quantization

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@inproceedings{e629a15dfd044d90857e840ea6b37e4b,

title = "Generalized Matrix Learning Vector Quantizer for the Analysis of Spectral Data",

abstract = "The analysis of spectral data constitutes new challenges for machine learning algorithms due to the functional nature of the data. Special attention is paid to the metric used in the analysis. Recently, a prototype based algorithm has been proposed which allows the integration of a full adaptive matrix in the metric. In this contribution we study this approach with respect to band matrices and its use for the analysis of functional spectral data. The method is tested on data taken from food chemistry and satellite image data.",

keywords = "spectral data, band matrices, GMLVQ, metric adaptation, Learning Vector Quantization",

author = "Petra Schneider and Frank-Michael Schleif and Thomas Villmann and Michael Biehl",

note = "Relation: http://www.rug.nl/informatica/organisatie/overorganisatie/iwi Rights: University of Groningen. Research Institute for Mathematics and Computing Science (IWI)",

year = "2008",

language = "English",

pages = "451--456",

editor = "Michel Verleysen",

booktitle = "Proc. European Symposium on Artificial Neural Networks",

publisher = "d-side publishing",

}

Generalized Matrix Learning Vector Quantizer for the Analysis of Spectral Data. / Schneider, Petra; Schleif, Frank-Michael; Villmann, Thomas et al.
Proc. European Symposium on Artificial Neural Networks: ESANN 2008. ed. / Michel Verleysen. d-side publishing, 2008. p. 451-456.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Generalized Matrix Learning Vector Quantizer for the Analysis of Spectral Data

AU - Schneider, Petra

AU - Schleif, Frank-Michael

AU - Villmann, Thomas

AU - Biehl, Michael

N1 - Relation: http://www.rug.nl/informatica/organisatie/overorganisatie/iwi Rights: University of Groningen. Research Institute for Mathematics and Computing Science (IWI)

PY - 2008

Y1 - 2008

N2 - The analysis of spectral data constitutes new challenges for machine learning algorithms due to the functional nature of the data. Special attention is paid to the metric used in the analysis. Recently, a prototype based algorithm has been proposed which allows the integration of a full adaptive matrix in the metric. In this contribution we study this approach with respect to band matrices and its use for the analysis of functional spectral data. The method is tested on data taken from food chemistry and satellite image data.

AB - The analysis of spectral data constitutes new challenges for machine learning algorithms due to the functional nature of the data. Special attention is paid to the metric used in the analysis. Recently, a prototype based algorithm has been proposed which allows the integration of a full adaptive matrix in the metric. In this contribution we study this approach with respect to band matrices and its use for the analysis of functional spectral data. The method is tested on data taken from food chemistry and satellite image data.

KW - spectral data

KW - band matrices

KW - GMLVQ

KW - metric adaptation

KW - Learning Vector Quantization

M3 - Conference contribution

SP - 451

EP - 456

BT - Proc. European Symposium on Artificial Neural Networks

A2 - Verleysen, Michel

PB - d-side publishing

ER -

Generalized Matrix Learning Vector Quantizer for the Analysis of Spectral Data

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Keywords

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