数据应该尽可能简单,但不能变得更简单:选择减小维度的方法及其参数可能会影响基于跑者运动学的跑者聚类
Adrian R Rivadulla1, Xi Chen2, Dario Cazzola1
1Department for Health, University of Bath, Bath, UK.
Journal of biomechanics
|November 21, 2024
概括
主要组件分析 (PCA) 和自动编码器是影响跑步步态集群的维度减小方法. 直接PCA为生物机械分析提供了压缩,重建和集群分离的最佳平衡.
科学领域:
- 生物力学 生物力学
- 数据科学数据科学数据科学
- 运动科学 运动科学 运动科学
背景情况:
- 减小尺寸对于生物力学中高效的聚类至关重要.
- 主要组件分析 (PCA) 是常用的,但可能不是最佳的.
- 评估像自动编码器这样的替代方法是必要的.
研究的目的:
- 为了比较PCA和基于自编码器的维度缩小,用于运行步态分析.
- 评估数据压缩,重建质量和聚类结果.
- 在生物力学中指导选择缩小维度的技术.
主要方法:
- 评估了直接PCA,福里埃PCA和一个前自动编码器 (AE).
- 使用差异解释标准和信号误差评估重建质量.
- 应用聚合层次分类对84名参与者的运行动力学数据.
- 比较不同尺寸缩小方法的聚类结果.
主要成果:
- 受欢迎的差异解释标准可以产生有意义的重建错误.
- 直接PCA,福里埃PCA和AE产生了不同的集群,突出了方法的灵敏性.
- 保持99%的差异的直接PCA为压缩,重建和集群分离提供了最佳的权衡.
结论:
- 重建错误评估对于明智选择减小维度组件至关重要.
- 不同的缩小尺寸的技术产生不同的集群结果,需要谨慎的解释.
- 直接PCA是一种强大的步态分析方法,平衡多个性能指标.
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