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Conjugate gradient algorithms for minor subspace analysis

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.
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https://hal.inria.fr/hal-00945274
Contributor : Roland Badeau <>
Submitted on : Monday, March 24, 2014 - 4:07:22 PM
Last modification on : Wednesday, October 14, 2020 - 1:11:49 PM
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Roland Badeau, Bertrand David, Gael Richard. Conjugate gradient algorithms for minor subspace analysis. Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2007, Honolulu, Hawaii, United States. pp.1013--1016. ⟨hal-00945274⟩

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