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Proceedings Paper

Canonical Correlations And Generalized SVD: Applications And New Algorithms
Author(s): L. Magnus Ewerbring; Franklin T. Luk
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Paper Abstract

In this paper we consider canonical correlations and a generalization of the singular value decomposition (SVD) that involves three matrices. We show how the two matrix problems are related and how they can be used in important applications such as weighted least squares and optimal prediction. We present two new computational procedures for the problems based on implicit SVD methods for triple matrix products. Our algorithms are well suited for parallel implementation.

Paper Details

Date Published: 16 December 1989
PDF: 17 pages
Proc. SPIE 0977, Real-Time Signal Processing XI, (16 December 1989); doi: 10.1117/12.948572
Show Author Affiliations
L. Magnus Ewerbring, Cornell University (United States)
Franklin T. Luk, Cornell University (United States)


Published in SPIE Proceedings Vol. 0977:
Real-Time Signal Processing XI
J. P. Letellier, Editor(s)

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