# Analysis of the robustness of NMF algorithms

arXiv: https://arxiv.org/abs/2106.02213 · HTML: https://mlsanity.com/paper/2106.02213/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 4 Jun 2021.

## Builds on (most cited first, 2 of 2)

- [The Why and How of Nonnegative Matrix Factorization](https://mlsanity.com/paper/1401.5226/) (2014)
- [A Review Paper: Noise Models in Digital Image Processing](https://mlsanity.com/paper/1505.03489/) (2015)

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

- [Contaminated Images Recovery by Implementing Non-negative Matrix Factorisation](https://mlsanity.com/paper/2211.04247/) (2022)

## Abstract (arXiv)

We examine three non-negative matrix factorization techniques; L2-norm, L1-norm, and L2,1-norm. Our aim is to establish the performance of these different approaches, and their robustness in real-world applications such as feature selection while managing computational complexity, sensitivity to noise and more. We thoroughly examine each approach from a theoretical perspective, and examine the performance of each using a series of experiments drawing on both the ORL and YaleB datasets. We examine the Relative Reconstruction Errors (RRE), Average Accuracy and Normalized Mutual Information (NMI) as criteria under a range of simulated noise scenarios.