# Sketching for low-rank nonnegative matrix approximation: Numerical study

arXiv: https://arxiv.org/abs/2201.11154 · HTML: https://mlsanity.com/paper/2201.11154/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2022.

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

- [When big data actually are low-rank, or entrywise approximation of certain function-generated matrices](https://mlsanity.com/paper/2407.03250/) (2024)

## Abstract (arXiv)

We propose new approximate alternating projection methods, based on randomized sketching, for the low-rank nonnegative matrix approximation problem: find a low-rank approximation of a nonnegative matrix that is nonnegative, but whose factors can be arbitrary. We calculate the computational complexities of the proposed methods and evaluate their performance in numerical experiments. The comparison with the known deterministic alternating projection methods shows that the randomized approaches are faster and exhibit similar convergence properties.