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. 
John Komar and Romain H\'erault and Ludovic Seifert
stat.ML cs.CV cs.LG physics.data-an stat.AP