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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.
Corporate Author 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
Series Volume Series Issue Edition
ISSN ISBN 978-1-58603-466-5 Medium
Area Expedition Conference
Notes MV;IAM;MILAB Approved no
Call Number (up) IAM @ iam @ VGR2004 Serial 1663
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