Total Least Squares Methods
Total Least Squares Methods
Recent advances in total least squares approaches for solving various errors-in-variables modeling problems are reviewed, with emphasis on the following generalizations: 1. the use of weighted norms as a measure of the data perturbation size, capturing prior knowledge about uncertainty in the data; 2. the addition of constraints on the perturbation to preserve the structure of the data matrix, motivated by structured data matrices occurring in signal and image processing, systems and control, and computer algebra; 3. the use of regularization in the problem formulation, aiming at stabilizing the solution by decreasing the effect due to intrinsic ill-conditioning of certain problems.
212-217
Markovsky, Ivan
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Sima, Diana M.
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Van Huffel, Sabine
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March 2010
Markovsky, Ivan
7d632d37-2100-41be-a4ff-90b92752212c
Sima, Diana M.
0feec716-5587-456c-b2be-152853a370d3
Van Huffel, Sabine
8814fa15-3922-4a5a-9ba5-c2ea63ceeaf7
Markovsky, Ivan, Sima, Diana M. and Van Huffel, Sabine
(2010)
Total Least Squares Methods.
Wiley Interdisciplinary Reviews: Computational Statistics, 2 (2), .
Abstract
Recent advances in total least squares approaches for solving various errors-in-variables modeling problems are reviewed, with emphasis on the following generalizations: 1. the use of weighted norms as a measure of the data perturbation size, capturing prior knowledge about uncertainty in the data; 2. the addition of constraints on the perturbation to preserve the structure of the data matrix, motivated by structured data matrices occurring in signal and image processing, systems and control, and computer algebra; 3. the use of regularization in the problem formulation, aiming at stabilizing the solution by decreasing the effect due to intrinsic ill-conditioning of certain problems.
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tls-review-published.pdf
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Published date: March 2010
Organisations:
Southampton Wireless Group
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Local EPrints ID: 267223
URI: http://eprints.soton.ac.uk/id/eprint/267223
PURE UUID: 46fe4ee4-6450-49fe-9123-fc3f67e49263
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Date deposited: 31 Mar 2009 11:10
Last modified: 14 Mar 2024 08:45
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Author:
Ivan Markovsky
Author:
Diana M. Sima
Author:
Sabine Van Huffel
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