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Conjugate gradient algorithms for minor subspace analysis

  • Telecom Paris

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We introduce a conjugate gradient method for estimating and tracking the minor eigenvector of a data correlation matrix. This new algorithm is less computationally demanding and converges faster than other methods derived from the conjugate gradient approach. It can also be applied in the context of minor subspace tracking, as a pre-processing step for the YAST algorithm, in order to enhance its performance. Simulations show that the resulting algorithm converges much faster than existing minor subspace trackers.

Original languageEnglish
Title of host publication2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
PagesIII1013-III1016
DOIs
Publication statusPublished - 6 Aug 2007
Event2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 - Honolulu, HI, United States
Duration: 15 Apr 200720 Apr 2007

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume3
ISSN (Print)1520-6149

Conference

Conference2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
Country/TerritoryUnited States
CityHonolulu, HI
Period15/04/0720/04/07

Keywords

  • Conjugate gradient methods
  • Minor subspace analysis
  • Subspace tracking

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