Key point selection and clustering of swimmer coordination through Sparse Fisher-EM
Machine Learning
2014-01-08 v1 Computer Vision and Pattern Recognition
Machine Learning
Data Analysis, Statistics and Probability
Applications
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.
Keywords
Cite
@article{arxiv.1401.1489,
title = {Key point selection and clustering of swimmer coordination through Sparse Fisher-EM},
author = {John Komar and Romain Hérault and Ludovic Seifert},
journal= {arXiv preprint arXiv:1401.1489},
year = {2014}
}
Comments
Presented at ECML/PKDD 2013 Workshop on Machine Learning and Data Mining for Sports Analytics (MLSA2013)