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Robust Nonlinear Canonical Correlation Analysis: Application to Seasonal Climate Forecasting : Volume 15, Issue 1 (27/02/2008)

By Cannon, A. J.

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Book Id: WPLBN0003985694
Format Type: PDF Article :
File Size: Pages 12
Reproduction Date: 2015

Title: Robust Nonlinear Canonical Correlation Analysis: Application to Seasonal Climate Forecasting : Volume 15, Issue 1 (27/02/2008)  
Author: Cannon, A. J.
Volume: Vol. 15, Issue 1
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Historic
Publication Date:
2008
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Hsieh, W. W., & Cannon, A. J. (2008). Robust Nonlinear Canonical Correlation Analysis: Application to Seasonal Climate Forecasting : Volume 15, Issue 1 (27/02/2008). Retrieved from http://www.worldebookfair.com/


Description
Description: Meteorological Service of Canada, Environment Canada, 201-401 Burrard Street, Vancouver, BC V6C 3S5, Canada. 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.


Summary
Robust nonlinear canonical correlation analysis: application to seasonal climate forecasting

Excerpt
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