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

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...