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Communication Dans Un Congrès Année : 2021

Sparse mixture of von Mises-Fisher distribution

Résumé

Mixtures of von Mises-Fisher distributions can be used to cluster data on the unit hypersphere. This is particularly adapted for high-dimensional directional data such as texts. We propose in this article to estimate a von Mises mixture using a l1 penalized likelihood. This leads to sparse prototypes that improve both clustering quality and interpretability. We introduce an expectation-maximisation (EM) algorithm for this estimation and show the advantages of the approach on real data benchmark. We propose to explore the trade-off between the sparsity term and the likelihood one with a simple path following algorithm.

Dates et versions

hal-03678633 , version 1 (25-05-2022)

Identifiants

Citer

Florian Barbaro, Fabrice Rossi. Sparse mixture of von Mises-Fisher distribution. ESANN 2021 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Oct 2021, Bruges, Belgium. pp.263-268, ⟨10.14428/esann/2021.ES2021-115⟩. ⟨hal-03678633⟩
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