Key point selection and clustering of swimmer coordination through Sparse Fisher-EM

Abstract : To answer the existence of optimal swimmer learning/teaching strategies, this work introduces a two-level clustering in order to analyze temporal dynamics of motor learning in breaststroke swimming. Each level have been performed through Sparse Fisher-EM, a unsupervised framework which can be applied efficiently on large and correlated datasets. The induced sparsity selects key points of the coordination phase without any prior knowledge.
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https://hal-normandie-univ.archives-ouvertes.fr/hal-02351767
Contributeur : Romain Hérault <>
Soumis le : mercredi 6 novembre 2019 - 15:16:28
Dernière modification le : jeudi 7 novembre 2019 - 01:32:43

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  • HAL Id : hal-02351767, version 1
  • ARXIV : 1401.1489

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John Komar, Romain Hérault, Ludovic Seifert. Key point selection and clustering of swimmer coordination through Sparse Fisher-EM. ECML/PKDD 2013 Workshop on Machine Learning and Data Mining for Sports Analytics (MLSA2013), Sep 2013, Praha, Czech Republic. ⟨hal-02351767⟩

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