Name
Title | ||
|---|---|---|
On Truncated-SVD-like Sparse Solutions to Least-Squares Problems of Arbitrary Dimensions |
Abstract | ||
|---|---|---|
We describe two algorithms for computing a sparse solution to a least-squares problem where the coefficient matrix can have arbitrary dimensions. We show that the solution vector obtained by our algorithms is close to the solution vector obtained via the truncated SVD approach. |
Year | Venue | Field |
|---|---|---|
2012 | arXiv: Data Structures and Algorithms | Least squares,Applied mathematics,Singular value decomposition,Discrete mathematics,Coefficient matrix,Sparse approximation,Theoretical computer science,Mathematics |
DocType | Volume | Citations |
Journal | abs/1201.0073 | 0 |
PageRank | References | Authors |
0.34 | 0 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christos Boutsidis | 1 | 610 | 33.37 |