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Low-Rank Matrix Approximation and Subspace Tracking

  • Patrick Dewilde
  • Alle-Jan van der Veen
Chapter

Abstract

The usual way to compute a low-rank approximant of a matrix H is to take its singular value decomposition (SVD) and truncate it by setting the small singular values equal to 0. However, the SVD is computationally expensive. Using the Hankel-norm model reduction techniques in chapter 10, we can devise a much simpler generalized Schurtype algorithm to compute similar low-rank approximants. Since rank approximation plays an important role in many linear algebra applications, we devote an independent chapter to this topic, even though this leads to some overlap with previous chapters.

Keywords

Singular Value Decomposition Elementary Rotation Subspace Estimate Subspace Tracking Principal Subspace 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 1998

Authors and Affiliations

  • Patrick Dewilde
    • 1
  • Alle-Jan van der Veen
    • 1
  1. 1.DIMESDelft University of TechnologyDelftThe Netherlands

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