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Subspace Optimization Techniques for Classification Problems
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2003 (English)Report (Other academic)
Abstract [en]

The nonlinear conjugate gradients method is a very powerful program in the search for Bayes error optimal linear subspaces for classification problems. In this report, techniques to find linear subspaces where the classification error is minimized are surveyed. Summary statistics models of normal populations are used to form smooth, non-convex objective functions of a linear transformation that reduces the dimensionality. Objective functions that are based on the Mahalanobis or Bhattacharyya distances and that are closely related to the probability of misclassification are derived, as well as their subspace gradients. Different approaches to minimize those objective functions are investigated: Householder and Givens parameterizations as well as steepest descent and conjugate gradient methods. The methods are evaluated on experimental data with respect to convergence rate and subspace classification accuracy.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2003. , 71 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2534
Keyword [en]
Pattern Recognition
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-55970ISRN: LiTH-ISY-R-2534OAI: oai:DiVA.org:liu-55970DiVA: diva2:316766
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-09-04Bibliographically approved

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Lindgren, David

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf