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.

Presented at ECML/PKDD 2013 Workshop on Machine Learning and Data Mining for Sports Analytics (MLSA2013)

References in corpus (1)

• Discriminative variable selection for clustering with the sparse Fisher-EM algorithm