NeurIPS 2015poster31 citations
Column Selection via Adaptive Sampling
Saurabh Paul, Malik Magdon-Ismail, Petros Drineas
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.
BibTeX
@inproceedings{NIPS2015_d3957710,
author = {Paul, Saurabh and Magdon-Ismail, Malik and Drineas, Petros},
booktitle = {Advances in Neural Information Processing Systems},
editor = {C. Cortes and N. Lawrence and D. Lee and M. Sugiyama and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Column Selection via Adaptive Sampling},
url = {https://proceedings.neurips.cc/paper_files/paper/2015/file/d395771085aab05244a4fb8fd91bf4ee-Paper.pdf},
volume = {28},
year = {2015}
}