Name
Abstract | ||
|---|---|---|
Traditionally, the singular value decomposition (SVD) has been used in rank and subspace tracking methods. However, the SVD is computationally costly, especially when the problem is recursive in nature and the size of the matrix is large. The truncated ULV decomposition (TULV) is an alternative to the SVD. It provides a good approximation to subspaces for the data matrix and can be modified quickly to reflect changes in the data. It also reveals the rank of the matrix. This paper presents a TULV updating algorithm. The algorithm is most efficient when the matrix is of low rank. Numerical results are presented that illustrate the accuracy of the algorithm. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1016/j.mcm.2006.02.011 | Mathematical and Computer Modelling |
Keywords | Field | DocType |
subspace tracking method,singular value decomposition,rank estimation,subspace tracking,modifying decompositions,numerical result,truncated ulv decomposition,ulv decomposition,efficient algorithm,low rank,good approximation,data matrix | Singular value decomposition,Mathematical optimization,Subspace topology,Matrix (mathematics),Algorithm,Linear subspace,Low-rank approximation,Mathematics,Recursion | Journal |
Volume | Issue | ISSN |
44 | 7-8 | Mathematical and Computer Modelling |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hasan Erbay | 1 | 11 | 5.32 |