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Additionally, tests involving electrode x condition interactions were carried out on normalized data using the vector normalization procedure [62], [63].
Several pre-processing methods, including Savitsky-Golay smoothing (2nd order polynomial function with 9 points), first-order and second-order derivative spectra [ 31], and two different normalization methods (Standard normal variate (SNV) and vector normalization) [ 32, 33] were applied to examine effects, if any, on the calibration models.
Vector normalization.
Vector normalization with mean normalization: Vector normalization followed by mean removal.
Vector normalization with mean normalization: Vector normalization followed by mean removal. .
Vector normalization: Divide each value in a vector with the vector's magnitude.
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It is designed for the deviation problem of the discriminant vector and the separability problem caused by sample vectors normalization when different sub-classes are located in different low-dimensional subspaces or manifolds.
To overcome the separability problem caused by sample fusion in the process of sample vectors normalization, this paper presents a unit super spherical cap discriminant analysis in the range space of the total scatter matrix.
After the I-vectors normalization, the different EAs and the TLBO technique have been applied in order to regroup the extracted I-vectors into an optimal number of clusters.
Vector-normalization is necessary, because "condition*location" interactions can result from multiplicative changes in source strength between conditions without specific differences concerning locations [ 53].
To merge the data from different sensors into a single feature vector, some normalization method should be employed.
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