A dimension reduction method in kernel discriminant analysis is presented, based on the concept of dimension reduction subspace. Examples of application are discussed.

The limit behavior of the conditional probability of error of linear and quadratic discriminant analyses is studied under wide assumptions on the class conditional distributions. Results obtained may help to explain analytically the behavior in applications of linear and quadratic discrimination...

Dimension reduction transformations in discriminant analysis are introduced. Their properties, as well as sufficient conditions for their characterization, are studied. Special attention is given to the continuous case, of particular importance in applications. An effective data based dimension...