# Applying separative non-negative matrix factorization to extra-financial data

arXiv: https://arxiv.org/abs/2206.04350 · HTML: https://mlsanity.com/paper/2206.04350/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 5 references that are in the map.
- First posted on arXiv 9 Jun 2022.

## Builds on (most cited first, 1 of 1)

- [Block Coordinate Descent for Sparse NMF](https://mlsanity.com/paper/1301.3527/) (2013)

## Abstract (arXiv)

We present here an original application of the non-negative matrix factorization (NMF) method, for the case of extra-financial data. These data are subject to high correlations between co-variables, as well as between observations. NMF provides a much more relevant clustering of co-variables and observations than a simple principal component analysis (PCA). In addition, we show that an initial data separation step before applying NMF further improves the quality of the clustering.