An Optimization Criterion for Generalized Marginal Fisher Analysis on Undersampled Problems

Marginal Fisher analysis （MFA） not only aims to maintain the original relations of neighboring data points of the same class but also wants to keep away neighboring data points of the different classes.MFA can effectively overcome the limitation of linear discriminant analysis （LDA） due to data dist...

Full description

Saved in:
Bibliographic Details
Published in:International journal of automation and computing 2011-05, Vol.8 (2), p.193-200
Main Authors: Yang, Wu-Yi, Liu, Sheng-Xing, Jin, Tai-Song, Xu, Xiao-Mei
Format: Article
Language:English
Subjects:
Algorithms
CAE) and Design
Computer Applications
Computer-Aided Engineering (CAD
Control
Criteria
Data points
Discriminant analysis
Engineering
Face recognition
Mathematical analysis
Mathematical models
Mechatronics
MFA
Optimization
Robotics
优化准则
优化标准
广义
投影方向
数据分布
线性判别分析
边际分析
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Marginal Fisher analysis （MFA） not only aims to maintain the original relations of neighboring data points of the same class but also wants to keep away neighboring data points of the different classes.MFA can effectively overcome the limitation of linear discriminant analysis （LDA） due to data distribution assumption and available projection directions.However,MFA confronts the undersampled problems.Generalized marginal Fisher analysis （GMFA） based on a new optimization criterion is presented,which is applicable to the undersampled problems.The solutions to the proposed criterion for GMFA are derived,which can be characterized in a closed form.Among the solutions,two specific algorithms,namely,normal MFA （NMFA） and orthogonal MFA （OMFA）,are studied,and the methods to implement NMFA and OMFA are proposed.A comparative study on the undersampled problem of face recognition is conducted to evaluate NMFA and OMFA in terms of classification accuracy,which demonstrates the effectiveness of the proposed algorithms.
ISSN:1476-8186
1751-8520
DOI:10.1007/s11633-011-0573-5