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

Author

Listed:
  • P Fogel

    (Advestis)

  • C Geissler

    (Advestis)

  • P Cotte

    (Advestis)

  • G Luta

    (GU - Georgetown University [Washington])

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.

Suggested Citation

  • P Fogel & C Geissler & P Cotte & G Luta, 2021. "Applying separative non-negative matrix factorization to extra-financial data," Post-Print hal-03141876, HAL.
    • Handle: RePEc:hal:journl:hal-03141876
    Note: View the original document on HAL open archive server: https://hal.science/hal-03141876v2
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    References listed on IDEAS

    as
    1. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
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