Name
Abstract | ||
|---|---|---|
In this paper, an extension of the structured total least-squares (STLS) approach for non-linearly structured matrices is presented in the so-called 'Riemannian singular value decomposition' (RiSVD) framework. It is shown that this type of STLS problem can be solved by solving a set of Riemannian SVD equations. For small perturbations the problem can be reformulated into finding the smallest singular value and the corresponding right singular vector of this Riemannian SVD. A heuristic algorithm is proposed. Some examples of Vandermonde-type matrices are used to demonstrate the improved accuracy of the obtained parameter estimator when compared to other methods such as least squares (LS) or total least squares (TLS). Copyright (C) 2002 John Wiley Sons, Ltd. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1002/nla.276 | NUMERICAL LINEAR ALGEBRA WITH APPLICATIONS |
Field | DocType | Volume |
Least squares,Singular value decomposition,Mathematical optimization,Singular value,Heuristic (computer science),Matrix (mathematics),Total least squares,Perturbation (astronomy),Mathematics,Estimator | Journal | 9 |
Issue | ISSN | Citations |
4 | 1070-5325 | 4 |
PageRank | References | Authors |
0.67 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Philippe Lemmerling | 1 | 131 | 22.31 |
| S. Van Huffel | 2 | 260 | 32.75 |
| Bart De Moor | 3 | 5541 | 474.71 |