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Author
Fernando Vilariño; Debora Gil; Petia Radeva
Title
A Novel FLDA Formulation for Numerical Stability Analysis
Type
Book Chapter
Year
2004
Publication
Recent Advances in Artificial Intelligence Research and Development
Abbreviated Journal
Volume
113
Issue
Pages
77-84
Keywords
Supervised Learning; Linear Discriminant Analysis; Numerical Stability; Computer Vision
Abstract
Fisher Linear Discriminant Analysis (FLDA) is one of the most popular techniques used in classification applying dimensional reduction. The numerical scheme involves the inversion of the within-class scatter matrix, which makes FLDA potentially ill-conditioned when it becomes singular. In this paper we present a novel explicit formulation of FLDA in terms of the eccentricity ratio and eigenvector orientations of the within-class scatter matrix. An analysis of this function will characterize those situations where FLDA response is not reliable because of numerical instability. This can solve common situations of poor classification performance in computer vision.
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Thesis
Publisher
IOS Press
Place of Publication
Editor
J. Vitrià, P. Radeva and I. Aguiló
Language
Summary Language
Original Title
Series Editor
Series Title
Abbreviated Series Title
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Series Issue
Edition
ISSN
ISBN
978-1-58603-466-5
Medium
Area
Expedition
Conference
Notes
MV;IAM;MILAB
Approved
no
Call Number
IAM @ iam @ VGR2004
Serial
1663
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