贝叶斯标量对张量回归使用塔克分解用于稀疏空间建模的塔克分解
Daniel A Spencer1, Rene Gutierrez2, Rajarshi Guhaniyogi3
1Winchester, MA 01890, United States.
Biostatistics (Oxford, England)
|December 23, 2025
概括
本研究介绍了一种使用塔克张量分解的贝叶斯方法,用于建模高维,稀疏的张量数据. 这种新的方法改善了复杂数据集的统计建模和分析,特别是在神经成像中.
科学领域:
- 统计建模 统计建模
- 机器学习 机器学习
- 神经成像分析分析神经成像分析
背景情况:
- 高维数据和张量固有的稀疏性给统计建模带来了挑战.
- 现有的方法很难有效地描述张量共变量和标量响应之间的关联.
- 规范化技术通常是必要的,但可能是复杂的实施.
研究的目的:
- 提出一个高效的贝叶斯方法,用于模拟与张量共变量的标量响应.
- 利用塔克张量分解来保持空间关系并减少模型参数.
- 在贝叶斯框架内应用规范化方法来进行可靠的分析.
主要方法:
- 使用塔克张量分解来处理张量系数的空间结构.
- 实施贝叶斯的方法,以高效的参数估计和不确定性量化.
- 将拟议的方法与使用模拟数据的现有张量回归技术进行比较.
主要成果:
- 拟议的贝叶斯方法证明了用张量共变量对标量反应的高效建模.
- 塔克分解有效地保留空间信息,同时减少模型复杂性.
- 与其他方法相比,模拟数据分析显示出具有竞争力的或改进的性能.
- 使用阿尔茨海默氏症数据神经影像计划数据进行的神经影像分析显示,推断性能得到了增强.
结论:
- 开发的贝叶斯法为分析高维,稀疏的张量数据提供了有效的解决方案.
- 塔克张量分解是保存张量系数中的空间关系的一个有价值的工具.
- 该方法对神经成像和其他需要复杂统计建模的领域的应用具有前景.
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