  A vast number of multicriteria decision making methods have been developed to
deal with the problem of ranking a set of alternatives evaluated in a
multicriteria fashion. Very often, these methods assume that the evaluation
among criteria is statistically independent. However, in actual problems, the
observed data may comprise dependent criteria, which, among other problems, may
result in biased rankings. In order to deal with this issue, we propose a novel
approach whose aim is to estimate, from the observed data, a set of independent
latent criteria, which can be seen as an alternative representation of the
original decision matrix. A central element of our approach is to formulate the
decision problem as a blind source separation problem, which allows us to apply
independent component analysis techniques to estimate the latent criteria.
Moreover, we consider TOPSIS-based approaches to obtain the ranking of
alternatives from the latent criteria. Results in both synthetic and actual
data attest the relevance of the proposed approach.
