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| Titel |
Nonlinear dimensionality reduction in climate data |
| VerfasserIn |
A. J. Gámez, C. S. Zhou, A. Timmermann, J. Kurths |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 11, no. 3 ; Nr. 11, no. 3 (2004-09-13), S.393-398 |
| Datensatznummer |
250009244
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| Publikation (Nr.) |
 copernicus.org/npg-11-393-2004.pdf |
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| Zusammenfassung |
| Linear methods of dimensionality reduction are useful tools for handling and
interpreting high dimensional data. However, the cumulative variance
explained by each of the subspaces in which the data space is decomposed may
show a slow convergence that makes the selection of a proper minimum number
of subspaces for successfully representing the variability of the process
ambiguous. The use of nonlinear methods can improve the embedding of
multivariate data into lower dimensional manifolds. In this article, a
nonlinear method for dimensionality reduction, Isomap, is applied to the sea
surface temperature and thermocline data in the tropical Pacific Ocean, where
the El Niño-Southern Oscillation (ENSO) phenomenon and the annual cycle
phenomena interact. Isomap gives a more accurate description of the manifold
dimensionality of the physical system. The knowledge of the minimum number of
dimensions is expected to improve the development of low dimensional models
for understanding and predicting ENSO. |
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