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