# Column Selection via Adaptive Sampling

arXiv: https://arxiv.org/abs/1510.04149 · HTML: https://mlsanity.com/paper/1510.04149/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2015.

## Built on by (most cited first, 1 of 1)

- [Two-way Spectrum Pursuit for CUR Decomposition and Its Application in Joint Column/Row Subset Selection](https://mlsanity.com/paper/2106.06983/) (2021)

## Abstract (arXiv)

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.