On-Line Singular Value Decomposition of Stochastic Process Covariances
1995 (English)Report (Other academic)
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.
LiTH-ISY-R, ISSN 1400-3902 ; 1762
Engineering and Technology
IdentifiersURN: urn:nbn:se:liu:diva-53419ISRN: LiTH-ISY-R-1762OAI: oai:DiVA.org:liu-53419DiVA: diva2:288273