Weighted fisher discriminant analysis in the input and feature spaces

Benyamin Ghojogh, Milad Sikaroudi, H. R. Tizhoosh, Fakhri Karray, Mark Crowley

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Fisher Discriminant Analysis (FDA) is a subspace learning method which minimizes and maximizes the intra- and inter-class scatters of data, respectively. Although, in FDA, all the pairs of classes are treated the same way, some classes are closer than the others. Weighted FDA assigns weights to the pairs of classes to address this shortcoming of FDA. In this paper, we propose a cosine-weighted FDA as well as an automatically weighted FDA in which weights are found automatically. We also propose a weighted FDA in the feature space to establish a weighted kernel FDA for both existing and newly proposed weights. Our experiments on the ORL face recognition dataset show the effectiveness of the proposed weighting schemes.

Original languageEnglish (US)
Title of host publicationImage Analysis and Recognition - 17th International Conference, ICIAR 2020, Proceedings
EditorsAurélio Campilho, Fakhri Karray, Zhou Wang
PublisherSpringer
Pages3-15
Number of pages13
ISBN (Print)9783030505158
DOIs
StatePublished - 2020
Event17th International Conference on Image Analysis and Recognition, ICIAR 2020 - Póvoa de Varzim, Portugal
Duration: Jun 24 2020Jun 26 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12132 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Image Analysis and Recognition, ICIAR 2020
Country/TerritoryPortugal
CityPóvoa de Varzim
Period6/24/206/26/20

Keywords

  • Automatically weighted FDA
  • Cosine-weighted FDA
  • Fisher Discriminant Analysis (FDA)
  • Kernel FDA
  • Manually weighted FDA

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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