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Conference Papers Year : 2013

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.

Dates and versions

hal-02351767 , version 1 (06-11-2019)

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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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