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| Titel |
Robust nonlinear canonical correlation analysis: application to seasonal climate forecasting |
| VerfasserIn |
A. J. Cannon, W. W. Hsieh |
| 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 ; 15, no. 1 ; Nr. 15, no. 1 (2008-02-27), S.221-232 |
| Datensatznummer |
250012566
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| Publikation (Nr.) |
 copernicus.org/npg-15-221-2008.pdf |
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| Zusammenfassung |
Robust variants of nonlinear canonical correlation analysis
(NLCCA) are introduced to improve performance on datasets with low
signal-to-noise ratios, for example those encountered when making
seasonal climate forecasts. The neural network model architecture of standard NLCCA
is kept intact, but the cost functions used to set the model parameters
are replaced with more robust variants. The Pearson product-moment
correlation in the double-barreled network is replaced by the biweight
midcorrelation, and the mean squared error (mse) in the inverse mapping
networks can be replaced by the mean absolute error (mae).
Robust variants of NLCCA are demonstrated on a synthetic dataset and
are used to forecast sea surface temperatures in the tropical Pacific
Ocean based on the sea level pressure field. Results suggest that
adoption of the biweight midcorrelation can lead to improved performance,
especially when a strong, common event exists in both predictor/predictand
datasets. Replacing the mse by the mae leads to improved performance
on the synthetic dataset, but not on the climate dataset except at
the longest lead time, which suggests that the appropriate cost function
for the inverse mapping networks is more problem dependent. |
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