在主要组件分析中的幻影振荡
1University College London (UCL) Queen Square Institute of Neurology, University College London, London WC1E 6BT, United Kingdom.
概括
主要元件分析 (PCA) 可以在连续数据中创建误导性的振荡模式,称为"幻象振荡",如神经科学数据集. 这些文物源于数据的平滑性和转移,影响分析,需要超越传统交叉验证的新检测方法.
科学领域:
- 神经科学是一个神经科学.
- 数据科学数据科学数据科学
- 统计 统计 统计 统计
背景情况:
- 主要组件分析 (PCA) 是一种广泛使用的缩小维度的技术.
- PCA组件通常被解释为高维数据中的低维模式.
- 这种解释对于时间序列和空间地图等连续数据是有问题的.
研究的目的:
- 为了确定连续数据中振荡主要组件的原因.
- 介绍"幻影振荡"的概念及其对数据分析的影响.
- 提出检测这些幻影振荡的方法.
主要方法:
- 数学证明来证明幻影振荡的产生.
- 分析数据属性,如平滑度和时间/空间变化.
- 评估用于幻影振荡检测的传统交叉验证.
主要成果:
- 时间或空间的平滑和变化被认为是幻影振荡的原因.
- 这些幻影振荡可以影响神经科学数据的解释.
- 标准的交叉验证方法无法检测幻影振荡.
结论:
- 来自PCA的模式可能不准确地反映底层连续数据.
- 幻影振荡是PCA在平滑,移动数据上的一个工件.
- 需要新的验证程序来识别和减轻数据分析中的幻影振荡.
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