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- W2080406989 abstract "Seeking an index characterizing the best-determined mode of variability leads to a natural generalization of principal-component analysis with an explicit metric characterizing the uncertainties of the data. This formalism, which distinguishes between state-space patterns and patterns of coefficients defining principal components, allows the more accurate data to exert a greater influence on the definition of the indices than they do in conventional principal-component analysis; in all other aspects, the new formalism is the same as the old. Within the context of the simple example of Bretherton and collaborators, metric-based principal-component analysis is shown to be capable of finding correlated patterns of variability in two different data sets." @default.
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- W2080406989 title "Metric-based principal components: data uncertainties" @default.
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- W2080406989 doi "https://doi.org/10.3402/tellusa.v48i4.12142" @default.
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