A new family of EVD tracking algorithms using Givens rotations

نویسندگان

  • Benoît Champagne
  • Qing-Guang Liu
چکیده

In this work, we derive new algorithms for tracking the eigenvalue decomposition (EVD) of a time-varying data co-variance matrix. These algorithms have parallel structures, low operation counts and good convergence behavior. Their main feature is the use of Givens rotations to update the eigenvector estimates. As a result, orthonormality of the latter can be maintained at all time, which is critical in the application of certain signal-subspace methods. The comparative performance of the new algorithms is illustrated by means of computer experiments.

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تاریخ انتشار 1996