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Learning Canonical Correlations
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9091-4724
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-9267-2191
n/a.
1995 (English)Report (Other academic)
Abstract [en]

This paper presents a novel learning algorithm that finds the linear combination of one set of multi-dimensional variates that is the best predictor, and at the same time finds the linear combination of another set which is the most predictable. This relation is known as the canonical correlation and has the property of being invariant with respect to affine transformations of the two sets of variates. The algorithm successively finds all the canonical correlations beginning with the largest one. It is shown that canonical correlations can be used in computer vision to find feature detectors by giving examples of the desired features. When used on the pixel level, the method finds quadrature filters and when used on a higher level, the method finds combinations of filter output that are less sensitive to noise compared to vector averaging.

Place, publisher, year, edition, pages
Linköping, Sweden: Linköping University, Department of Electrical Engineering , 1995. , 6 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 1761
Keyword [en]
Learning algorithms, Input-output signals, Correlation analysis
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-53336ISRN: LITH-ISY-R-1761OAI: oai:DiVA.org:liu-53336DiVA: diva2:288567
Available from: 2010-01-21 Created: 2010-01-20 Last updated: 2014-10-08Bibliographically approved

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Knutsson, HansBorga, Magnus

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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