# Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystr"om method

arXiv: https://arxiv.org/abs/2002.09073 · HTML: https://mlsanity.com/paper/2002.09073/

## Key facts

- Cited by 3 papers in the map (data through 13 Oct 2026).
- First posted on arXiv 21 Feb 2020.

## Built on by (most cited first, 3 of 3)

- [Recent and Upcoming Developments in Randomized Numerical Linear Algebra for Machine Learning](https://mlsanity.com/paper/2406.11151/) (2024)
- [Algorithmic Gaussianization through Sketching: Converting Data into Sub-gaussian Random Designs](https://mlsanity.com/paper/2206.10291/) (2022)
- [Randomized Dimension Reduction with Statistical Guarantees](https://mlsanity.com/paper/2310.01739/) (2023)

## Abstract (arXiv)

The Column Subset Selection Problem (CSSP) and the Nystr"om method are among the leading tools for constructing small low-rank approximations of large datasets in machine learning and scientific computing. A fundamental question in this area is: how well can a data subset of size k compete with the best rank k approximation? We develop techniques which exploit spectral properties of the data matrix to obtain improved approximation guarantees which go beyond the standard worst-case analysis. Our approach leads to significantly better bounds for datasets with known rates of singular value decay, e.g., polynomial or exponential decay. Our analysis also reveals an intriguing phenomenon: the approximation factor as a function of k may exhibit multiple peaks and valleys, which we call a multiple-descent curve. A lower bound we establish shows that this behavior is not an artifact of our analysis, but rather it is an inherent property of the CSSP and Nystr"om tasks. Finally, using the example of a radial basis function (RBF) kernel, we show that both our improved bounds and the multiple-descent curve can be observed on real datasets simply by varying the RBF parameter.