Column Selection via Adaptive Sampling
Abstract
Selecting a good column (or row) subset of massive data matrices has found many applications in data analysis and machine learning. We propose a new adaptive sampling algorithm that can be used to improve any relative-error column selection algorithm. Our algorithm delivers a tighter theoretical bound on the approximation error which we also demonstrate empirically using two well known relative-error column subset selection algorithms. Our experimental results on synthetic and real-world data show that our algorithm outperforms non-adaptive sampling as well as prior adaptive sampling approaches.
Cite
Text
Paul et al. "Column Selection via Adaptive Sampling." Neural Information Processing Systems, 2015.Markdown
[Paul et al. "Column Selection via Adaptive Sampling." Neural Information Processing Systems, 2015.](https://mlanthology.org/neurips/2015/paul2015neurips-column/)BibTeX
@inproceedings{paul2015neurips-column,
title = {{Column Selection via Adaptive Sampling}},
author = {Paul, Saurabh and Magdon-Ismail, Malik and Drineas, Petros},
booktitle = {Neural Information Processing Systems},
year = {2015},
pages = {406-414},
url = {https://mlanthology.org/neurips/2015/paul2015neurips-column/}
}