Publication date: 08/13/2020

Description of the Wide Linear Algorithm

Wide Linear discriminant analysis is performed as follows:

The data are standardized by subtracting group means and dividing by pooled standard deviations.

The singular value decomposition is used to obtain a principal component transformation matrix from the set of singular vectors.

The number of components retained represents a minimum of 0.9999 of the sum of the squared singular values.

A linear discriminant analysis is performed on the transformed data, where the data are not shifted by group means. This is a fast calculation because the pooled-within covariance matrix is diagonal.

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