  This work proposes the application of independent component analysis to the
problem of ranking different alternatives by considering criteria that are not
necessarily statistically independent. In this case, the observed data (the
criteria values for all alternatives) can be modeled as mixtures of latent
variables. Therefore, in the proposed approach, we perform ranking by means of
the TOPSIS approach and based on the independent components extracted from the
collected decision data. Numerical experiments attest the usefulness of the
proposed approach, as they show that working with latent variables leads to
better results compared to already existing methods
