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Consequently, the covariance matrix can be constructed directly using original matrix of features.
where Z ( k ) ∈ R q × p is the matrix of features with a reduced dimensionality.
As mentioned before, the calculation of the Mahalanobis distance depends on the size of the matrix of features.
The SOM models represent a multidimensional dataset by means of a bidimensional matrix of features, which can be explored for analysis and estimation purposes.
Different from the conventional ANN, SOM-based models represent a multidimensional data set by means of a bidimensional matrix of features, which may be applied for analysis and estimation purposes.
For each 12.8-s window, a 5 × M matrix of features, F C _ EEG, was obtained by concatenating the M 5 × 1 feature vectors corresponding to the M different channels.
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From the complete alignment of the 27 genomes, a matrix of feature presence/absence was computed using the bbFilter script distributed with Mauve, where each feature represented 50bp of unique sequence.
The projection from sensor space to the principal component space is performed through the matrix of selected feature vectors [Jolliffe, 2002] for the principal components as: (5) where V is the matrix of feature vectors mapping from the original recorded time-series to the reduced PCA-space.
A t f representation of a segment of a non-stationary signal can be seen as a matrix set of features with column and row wise relationships, holding discriminant information about the underlying process.
A general type of sparse model is shown in Eq. (1), where y is a vector with different observations; A is a matrix consisting of features from different classes; and x is a vector of coefficients corresponding to the observation vector y.
Mahalanobis distance calculates the distance between two samples based on their mean feature vectors and, and the covariance matrix of the features across all samples in the database.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com