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The PCA is one of a set of ordination techniques used in data reduction and summarization.
Factor analysis is often used in data reduction, for example to identify one factor (FPS = dependent variable) that explains most of the variance observed in a larger number of independent variables (chair rise, picking up a pen, putting a cardigan on and off, walking = independent variables).
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On the other hand, when luminous flamelets were attached to the burning surface, line-of-sigh-averaged NO concentrations (where a fixed path length is used in data anlysis) exhibited a rapid reduction with increased distance from the burning surface.
The analytical model is used in a data reduction scheme that relates experimental quantities to materials properties.
Principal Components Analysis, followed by sensitivity analysis with Monte Carlo simulation were used in the data reduction phase.
This dependence was significantly reduced in case of using the Hartman-Schijve relation in data reduction.
In this paper, PCA is not used as data reduction algorithm.
CATPCA is often used for data reduction.
PCA is a statistical technique used for data reduction.
The back-propagation ANN is used in signal processing, data reduction and optimization, interpretation and prediction of spectra and calibration [17].
This sequence of events violates the assumptions used in the reduction of data from common mode III tests.
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