Generalized matrix learning vector quantizer for the analysis of spectral data

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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.

Petra Schneider, Frank-Michael Schleif, Thomas Vil

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ESANN 2008 | Functional Spectral Data | Neural Networks | Spectral Data | Spectral Data Constitutes |

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Added	29 Oct 2010
Updated	29 Oct 2010
Type	Conference
Year	2008
Where	ESANN
Authors	Petra Schneider, Frank-Michael Schleif, Thomas Villmann, Michael Biehl

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Generalized matrix learning vector quantizer for the analysis of spectral data

ESANN 2008 | Functional Spectral Data | Neural Networks | Spectral Data | Spectral Data Constitutes |

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