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The POD employed herein is a specific POD for arbitrary multivariate data representation.
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The suboptimal performance of wavelets with regard to the approximation of multivariate data gave rise to new representation systems, specifically designed for data with anisotropic features.
A fundamental problem in neural network research, as well as in many other disciplines, is finding a suitable representation of multivariate data, i.e. random vectors.
A third effort addressed the question of how to obtain reduced dimension representations of multivariate data, albeit at the cost of loss of some information.
Thus, data processing of multivariate time series needs an efficient data representation method.
CAP is also more amenable to a graphical representation of the multivariate data.
Spectral variable selection is an important step in spectroscopic data analysis, as it tends to parsimonious data representation and can result in multivariate models with greater predictive ability.
Its subclasses comprise a univariate analysis model, bivariate analysis model, and multivariate analysis model by cardinality of the quality data representation slot.
By means of a linear array of ordered prototypes the 1D-SOM representation gives a convenient overview on relevant patterns in complex multivariate data.
Uni- and multivariate data analyses were applied.
Suppose, we have a set of multivariate data and we wish to test the multivariate outliers.
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