Name
Title | ||
|---|---|---|
Improved Low-rank Matrix Decompositions via the Subsampled Randomized Hadamard Transform |
Abstract | ||
|---|---|---|
We comment on two randomized algorithms for constructing low-rank matrix decompositions. Both algorithms employ the Subsampled Randomized Hadamard Transform [14]. The first algorithm appeared recently in [9]; here, we provide a novel analysis that significantly improves the approximation bound obtained in [9]. A preliminary version of the second algorithm appeared in [7]; here, we present a mild modification of this algorithm that achieves the same approximation bound but significantly improves the corresponding running time. |
Year | Venue | Keywords |
|---|---|---|
2011 | Clinical Orthopaedics and Related Research | data structure,randomized algorithm,matrix decomposition |
Field | DocType | Volume |
Randomized algorithm,Discrete mathematics,Combinatorics,Matrix (mathematics),Low-rank approximation,Hadamard transform,Mathematics | Journal | abs/1105.0 |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christos Boutsidis | 1 | 610 | 33.37 |