Introduction - If you have any usage issues, please Google them yourself
Dimensionality reduction is one of the important preprocessing
steps to handle high-dimensional data. Linear discriminant
analysis (LDA) is a classical and popular approach for this purpose.
LDA finds an optimal linear transformation, which maximizes
the ratio of the variance in the between-class distance to
the variance in the within-class distance. On the other hand,
in order to overcome the limitation in LDA resulting the
assumption of equal covariance, several heteroscedastic extensions,
such as heteroscedastic discriminant analysis (HDA), have
been proposed.