Theoretical analysis of linear discriminant analysis criteria

ÇEVİKALP H.

IEEE 14th Signal Processing and Communications Applications, Antalya, Türkiye, 16 - 19 Nisan 2006, ss.5-8 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/siu.2006.1659706
  • Basıldığı Şehir: Antalya
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.5-8
  • Eskişehir Osmangazi Üniversitesi Adresli: Hayır

Özet

The Fisher's Linear Discriminant Analysis (FLDA) is a successful linear feature extraction method which aims to maximize the between-class separability and to minimize the within-class variability. In order to accomplish its goal, FLDA maximizes the Fisher's Linear Discriminant Analysis criterion given in the paper. In this paper we first address the limitations of the classical FLDA criterion and then discuss new criterion functions which were introduced to overcome those limitations. Finally, we demonstrate the capability of the new criteria both theoretically and empirically.