Adaptive Mixture Model Reduction based on the Composite Transportation Dissimilarity

D'Ortenzio A., Manes C., Iuliis V. D., ORGUNER U.

26th International Conference on Information Fusion, FUSION 2023, South Carolina, United States Of America, 27 - 30 June 2023 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.23919/fusion52260.2023.10224099
  • City: South Carolina
  • Country: United States Of America
  • Keywords: Kullback-Leibler Divergence, Mixture Reduction, Model Selection, Optimal Transport Theory
  • Middle East Technical University Affiliated: Yes


Providing efficient yet accurate statistical models is a challenging problem in many applications. When elementary models are not sufficiently descriptive, mixtures of densities can be used. A complexity management issue arises when mixture models are employed: the number of components should be a trade-off between the complexity and the accuracy of the model. However, in general, it is not obvious how to determine the right number of mixture components for a specific application. In a previous work, theoretical foundations to address such a topic have been laid, grounded on the use of the Composite Transportation Dissimilarity between mixtures, and a preliminary criterion to manage the complexity of a mixture model has been proposed. In this paper, additional theoretical insights are provided that allow to formulate a novel adaptive mixture reduction algorithm. Numerical tests show that in most cases the new algorithm constitutes a significant improvement over the previous one.