Orthogonalized Fisher discriminant

被引：17

作者：

Song, FX ^{[1
]}

Liu, SH ^{[1
]}

Yang, JY ^{[1
]}

机构：

[1] Nanjing Univ Sci & Technol, Dept Comp Sci, Nanjing 210094, Peoples R China

来源：

PATTERN RECOGNITION | 2005年 / 38卷 / 02期

基金：

中国国家自然科学基金;

关键词：

linear discriminant; orthogonal set of vectors; nearest neighbor classifier; face recognition; pattern classification;

D O I：

10.1016/j.patcog.2004.06.007

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The Foley-Sammon discriminant (FSD) exhibits higher performance in face recognition than the Fisher linear discriminant due to its elimination of dependences among discriminant vectors. But its theory is complex and the calculation is time expensive. The orthogonalized Fisher discriminant (OFD), which also derives a set of orthogonal discriminant vectors, is very simple and easy to implement. Experiments show that OFD is more effective and efficient than FSD in pattern recognition. (C) 2004 Published by Elsevier Ltd on behalf of Pattern Recognition Society.

引用

页码：311 / 313

页数：3

共 4 条

[1] AN OPTIMAL TRANSFORMATION FOR DISCRIMINANT AND PRINCIPAL COMPONENT ANALYSIS [J].

DUCHENE, J ;

LECLERCQ, S .

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1988, 10 (06) :978-983

[2] OPTIMAL SET OF DISCRIMINANT VECTORS [J].

FOLEY, DH ;

SAMMON, JW .

IEEE TRANSACTIONS ON COMPUTERS, 1975, C 24 (03) :281-289

[3]

Hart, 2006, PATTERN CLASSIFICATI

[4] Face recognition based on the uncorrelated discriminant transformation [J].

Jin, Z ;

Yang, JY ;

Hu, ZS ;

Lou, Z .

PATTERN RECOGNITION, 2001, 34 (07) :1405-1416

← 1 →