Efficient Algorithms for Constructing an Interpolative Decomposition
Numerical Analysis
2022-06-08 v2 Numerical Analysis
Abstract
Low-rank approximations are essential in modern data science. The interpolative decomposition provides one such approximation. Its distinguishing feature is that it reuses columns from the original matrix. This enables it to preserve matrix properties such as sparsity and non-negativity. It also helps save space in memory. In this work, we introduce two optimized algorithms to construct an interpolative decomposition along with numerical evidence that they outperform the current state of the art.
Cite
@article{arxiv.2105.07076,
title = {Efficient Algorithms for Constructing an Interpolative Decomposition},
author = {Rishi Advani and Sean O'Hagan},
journal= {arXiv preprint arXiv:2105.07076},
year = {2022}
}
Comments
Disclaimer: we do not have any experiments on very large matrices, so these findings are only conclusive for relatively small matrices