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

P Fogel; C Geissler; P Cotte; G Luta

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arXiv · 2206.04350

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

P Fogel·C Geissler·P Cotte·G Luta

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Abstract

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.

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P Fogel, C Geissler, P Cotte, G Luta. 2022-06-09. Applying separative non-negative matrix factorization to extra-financial data. https://arxiv.org/abs/2206.04350

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