Editorial Type:
Article
Article Type:
Research Article

Nonlinear Principal Predictor Analysis: Application to the Lorenz System

Alex J. Cannon Meteorological Service of Canada, Vancouver, British Columbia, Canada

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Print Publication:
15 Feb 2006
DOI:
https://doi.org/10.1175/JCLI3634.1
Page(s):
579–589
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Abstract

Principal predictor analysis is a multivariate linear technique that fits between regression and canonical correlation analysis in terms of the complexity of its architecture. This study introduces a new neural network approach for performing nonlinear principal predictor analysis (NLPPA). NLPPA is applied to the Lorenz system of equations and is compared with nonlinear canonical correlation analysis (NLCCA) and linear multivariate models. Results suggest that NLPPA is capable of performing better than NLCCA when datasets are corrupted with noise. Also, NLPPA modes may be extracted in less time than NLCCA modes. NLPPA is recommended for prediction problems where a clear set of predictors and a clear set of predictands can be easily defined.

Corresponding author address: Alex J. Cannon, Meteorological Service of Canada, 201-401 Burrard St., Vancouver, BC V6C 3S5, Canada. Email: alex.cannon@ec.gc.ca

Abstract

Principal predictor analysis is a multivariate linear technique that fits between regression and canonical correlation analysis in terms of the complexity of its architecture. This study introduces a new neural network approach for performing nonlinear principal predictor analysis (NLPPA). NLPPA is applied to the Lorenz system of equations and is compared with nonlinear canonical correlation analysis (NLCCA) and linear multivariate models. Results suggest that NLPPA is capable of performing better than NLCCA when datasets are corrupted with noise. Also, NLPPA modes may be extracted in less time than NLCCA modes. NLPPA is recommended for prediction problems where a clear set of predictors and a clear set of predictands can be easily defined.

Corresponding author address: Alex J. Cannon, Meteorological Service of Canada, 201-401 Burrard St., Vancouver, BC V6C 3S5, Canada. Email: alex.cannon@ec.gc.ca

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