Computing low-dimensional signal subspaces

Ricardo D. Fierro; Per Christian Hansen

doi:10.1117/12.190894

28 October 1994 Computing low-dimensional signal subspaces

Ricardo D. Fierro, Per Christian Hansen

Proceedings Volume 2296, Advanced Signal Processing: Algorithms, Architectures, and Implementations V; (1994) https://doi.org/10.1117/12.190894
Event: SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, 1994, San Diego, CA, United States

Abstract

A two-sided (or complete) orthogonal decomposition of an m X n matrix A is a product of an orthogonal matrix, a triangular matrix, and another orthogonal matrix. Two examples are the URV and ULV decompositions. In this paper we present and analyze URV and ULV algorithms that are efficient whenever the numerical rank k of the matrix is much less than min(m,n). We also prove that good estimates of the singular vectors, needed in the algorithms, lead to good approximations of the singular subspaces of A.

Citation Download Citation

Ricardo D. Fierro and Per Christian Hansen "Computing low-dimensional signal subspaces", Proc. SPIE 2296, Advanced Signal Processing: Algorithms, Architectures, and Implementations V, (28 October 1994); https://doi.org/10.1117/12.190894

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