Improved Low-rank Matrix Decompositions via the Subsampled Randomized Hadamard Transform
Data Structures and Algorithms
2012-04-04 v3
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.
Cite
@article{arxiv.1105.0464,
title = {Improved Low-rank Matrix Decompositions via the Subsampled Randomized Hadamard Transform},
author = {Christos Boutsidis},
journal= {arXiv preprint arXiv:1105.0464},
year = {2012}
}
Comments
This paper has been withdrawn; an updated study is available by Boutsidis and Gittens: http://arxiv.org/abs/1204.0062