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Nonlinear Dimensionality Reduction Methods in Climate Data Analysis
[article]
2009
arXiv
pre-print
Linear dimensionality reduction techniques, notably principal component analysis, are widely used in climate data analysis as a means to aid in the interpretation of datasets of high dimensionality. These linear methods may not be appropriate for the analysis of data arising from nonlinear processes occurring in the climate system. Numerous techniques for nonlinear dimensionality reduction have been developed recently that may provide a potentially useful tool for the identification of
arXiv:0901.0537v1
fatcat:2ethc7ddtjdyxkbqzmtg64upwi