Key Point Selection and Clustering of Swimmer Coordination Through Sparse Fisher-EM

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.

• Publication year

  2014

• Venue

  MLSA@PKDD/ECML

• Publication date

  2014-01-07

• Fields of study

  Mathematics, Physics, Computer Science, Engineering

• Identifiers
  arXiv 1401.1489
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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