Self-organizing maps for the analysis of complex movement patterns

被引：55
|
作者：
Bauer, HU
Schollhorn, W
机构：
[1] MAX PLANCK INST STROMUNGSFORSCH,D-37018 GOTTINGEN,GERMANY
[2] UNIV FRANKFURT,SFB NICHTLINEARE DYNAM 185,D-6000 FRANKFURT,GERMANY
[3] UNIV FRANKFURT,INST BIOMECH,D-6000 FRANKFURT,GERMANY
来源：
NEURAL PROCESSING LETTERS | 1997年 / 5卷 / 03期
关键词：
cluster analysis; dimension-reduction; movement pattern; neighborhood preservation; SOM;
D O I：
10.1023/A:1009646811510
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We apply the Self-Organizing-Map-algorithm (SOM) as a central processing step in a new scheme for the characterisation of movement patterns of athletes. Due to its non-linear dimension reduction capabilities, the SOM outperforms a direct processing of the data as well as preprocessing using principal component analysis. Our results open the way to an objective assessment of movement patterns, with possible applications in the sport sciences as well as in medicine.
引用
收藏
页码：193 / 199
页数：7
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