title: The MDF discrimination measure: Fisher in disguise.
abstract: Recently, a discrimination measure for feature extraction for two-class data, called the maximum discriminating (MDF) measure (Talukder and Casasent [Neural Networks 14 (2001) 1201-1218]), was introduced. In the present paper, it is shown that the MDF discrimination measure produces exactly the same results as the classical Fisher criterion, on the condition that the two prior probabilities are chosen to be equal. The effect of unequal priors on the efficiency of the measures is also discussed.
authors.affiliation: Image Sciences Institute, University Medical Center Utrecht, HP E01.334, P.O. Box 85500, 3508 GA Utrecht, The Netherlands. marco@isi.uu.nl
authors.forename: Marco
authors.initials: M
authors.lastname: Loog
authors.forename: Robert P W
authors.initials: RP
authors.lastname: Duin
authors.forename: Max A
authors.initials: MA
authors.lastname: Viergever
mesh_terms: D000818:Animals; D016002:Discriminant Analysis; D006801:Humans; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D017711:Nonlinear Dynamics; D014894:Weights and Measures