  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.
