Communication Dans Un Congrès Année : 2020

Collaborative Clustering through Optimal Transport

Résumé

Very significant results have been achieved recently by exchanging information between multiple learners for clustering tasks. However, this approaches still suffer from a few issues regarding the choice of the information to trade, the stopping criteria and the trade-of between the information extracted from the data and the information exchanged by the models. We aim in this paper to address this issues through a novel approach propelled by the optimal transport theory. More particularly, the objective function is based on the Wasserstein metric, with a bidirectional transport of the information. This formulation not only leads to a high stability and an increase of the convergence speed, but also allows the learning of a stopping criteria. Extensive experiments were conducted on multiple data sets to evaluate the proposed method.

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hal-05000329 , version 1 (21-03-2025)

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Fatima Ezzahraa Ben Bouazza, Younès Bennani, Guénaël Cabanes, Abdelfettah Touzani. Collaborative Clustering through Optimal Transport. 29th International Conference on Artificial Neural Networks (ICANN 2020), Sep 2020, Bratislava, on line, Slovakia. pp.873-885, ⟨10.1007/978-3-030-61616-8_70⟩. ⟨hal-05000329⟩
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