Adaptive Mixture Model Reduction based on the Composite Transportation Dissimilarity

Date

2023-01-01

Author

D'Ortenzio, Alessandro
Manes, Costanzo
Iuliis, Vittorio De
Orguner, Umut

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Kullback-Leibler Divergence, Mixture Reduction, Model Selection, Optimal Transport Theory

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85171564281&origin=inward
https://hdl.handle.net/11511/105757

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Department of Electrical and Electronics Engineering, Conference / Seminar