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On-Line Singular Value Decomposition of Stochastic Process Covariances
n/a.
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
1995 (English)Report (Other academic)
Abstract [en]

This paper presents novel algorithms for finding the singular value decomposition (SVD) of a general covariance matrix by stochastic approximation. General in the sense that also non-square, between sets, covariance matrices are dealt with. For one of the algorithms, convergence is shown using results from stochastic approximation theory. Proofs of this sort, establishing both the point of equilibrium and its domain of attraction, have been reported very rarely for stochastic, iterative feature extraction algorithms.

Place, publisher, year, edition, pages
Linköping, Sweden: Linköping University, Department of Electrical Engineering , 1995. , 6 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 1762
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-53419ISRN: LiTH-ISY-R-1762OAI: oai:DiVA.org:liu-53419DiVA: diva2:288273
Available from: 2010-01-20 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

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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