Unsupervised classification in high dimension

Abstract : Dealing with large databases of galaxy spectra is a good example of a new problematic task in astrophysics. Current and forthcoming big surveys provide millions of spectra each containing thousands of wavelengths. These spectra must be confronted with physical and chemical models. This requires an unsupervised classification which is a dimensionality reduction in both the number of observations and parameters. In this poster, we present some approaches that we are implementing.
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https://hal.archives-ouvertes.fr/hal-01569733
Contributeur : Didier Fraix-Burnet <>
Soumis le : jeudi 27 juillet 2017 - 14:42:47
Dernière modification le : lundi 24 septembre 2018 - 16:04:03

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  • HAL Id : hal-01569733, version 1

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Didier Fraix-Burnet, Charles Bouveyron, Stéphane Girard, Julyan Arbel. Unsupervised classification in high dimension. European Week of Astronomy and Space Science (EWASS 2017), Jun 2017, Prague, Czech Republic. 2017, 〈http://eas.unige.ch/EWASS2017/index.jsp〉. 〈hal-01569733〉

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