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Proceedings Paper

Updating signal subspaces
Author(s): Christian H. Bischof; Gautam M. Shroff
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Paper Abstract

We develop an algorithm for adaptively estimating the noise subspace of a data matrix as is required in signal processing applications employing the ''signal subspace'' approach. The noise subspace is estimated using a rank-revealing QR factorization instead of the more expensive singular value or eigenvalue decompositions. Using incremental condition estimation to monitor the smallest singular values of triangular matrices we can update the rank-revealing triangular factorization inexpensively when new rows are added and old rows are deleted. Experiments demonstrate that the new approach usually requires 0(n2) work to update an n x n matrix and accurately tracks the noise subspace.

Paper Details

Date Published: 1 November 1990
PDF: 11 pages
Proc. SPIE 1348, Advanced Signal Processing Algorithms, Architectures, and Implementations, (1 November 1990); doi: 10.1117/12.23490
Show Author Affiliations
Christian H. Bischof, Argonne National Lab. (United States)
Gautam M. Shroff, Rensselaer Polytechnic Institute (United States)

Published in SPIE Proceedings Vol. 1348:
Advanced Signal Processing Algorithms, Architectures, and Implementations
Franklin T. Luk, Editor(s)

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