Updating rate of Jacobi singular value decomposition (SVD) arrays and data nonstationarity

Flavio Lorenzelli; Kung Yao

doi:10.1117/12.190853

28 October 1994 Updating rate of Jacobi singular value decomposition (SVD) arrays and data nonstationarity

Flavio Lorenzelli, Kung Yao

Proceedings Volume 2296, Advanced Signal Processing: Algorithms, Architectures, and Implementations V; (1994) https://doi.org/10.1117/12.190853
Event: SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, 1994, San Diego, CA, United States

Abstract

An effective updating algorithm for singular value decomposition, based on Jacobi rotations, has recently been proposed. This algorithm is composed of two basic steps: QR updating and rediagonalization. By proper interleaving these two operations, parallel implementations with very high updating rates are possible. In this paper, we are concerned with the behavior of this algorithm for nonstationary data, and the effect of the pipeline rate on tracking accuracy. In order to overcome the trade-off between accuracy and updating rate intrinsic in the original algorithm, we proposed two schemes which improve the overall performance when the rate of change of the data is high. In the `variable rotational rate' scheme, the number of Jacobi rotations per update is dynamically determined. The alternative approach is to make the forgetting factor variable and data-dependent. Behavior and performance of both schemes are discussed and compared.

Citation Download Citation

Flavio Lorenzelli and Kung Yao "Updating rate of Jacobi singular value decomposition (SVD) arrays and data nonstationarity", Proc. SPIE 2296, Advanced Signal Processing: Algorithms, Architectures, and Implementations V, (28 October 1994); https://doi.org/10.1117/12.190853

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