Self-Organizing Maps for the Analysis of Complex Movement Patterns
作者： H.U. Bauer , W. Schöllhorn
关键词:
摘要: We apply the Self-Organizing-Map-algorithm (SOM) as a central processing step in new scheme for characterisation of movement patterns athletes. Due to its non-linear dimension reduction capabilities, SOM outperforms direct data well preprocessing using principal component analysis. Our results open way an objective assessment patterns, with possible applications sport sciences medicine.
springer.com
acm.org
sci-hub.se 参考文章(7)
Th. Villmann, R. Der, Th. Martinetz, A Novel Approach to Measure the Topology Preservation of Feature Maps ICANN ’94. pp. 298- 301 ,(1994) , 10.1007/978-1-4471-2097-1_69
Hermann Haken, Principles of brain functioning ,(1995)
Hermann Haken, Principles of Brain Functioning: A Synergetic Approach to Brain Activity, Behavior and Cognition Springer Publishing Company, Incorporated. ,(2012)
Klaus Schulten, Thomas Martinetz, Daniel Barsky, Helge Ritter, Ronald Kates, Marcus Tesch, Neural Computation And Self Organizing Maps: An Introduction ,(1992)
Stefan H Holzreiter, Monika E Köhle, None, Assessment of gait patterns using neural networks Journal of Biomechanics. ,vol. 26, pp. 645- 651 ,(1993) , 10.1016/0021-9290(93)90028-D
James C. Bezdek, Nikhil R. Pal, An index of topological preservation for feature extraction Pattern Recognition. ,vol. 28, pp. 381- 391 ,(1995) , 10.1016/0031-3203(94)00111-X
H.-U. Bauer, K.R. Pawelzik, Quantifying the neighborhood preservation of self-organizing feature maps IEEE Transactions on Neural Networks. ,vol. 3, pp. 570- 579 ,(1992) , 10.1109/72.143371