图形规则化的张量回归:用于对图形上的多路数据的可解释建模的域识别框架
Yao Lei Xu1, Kriton Konstantinidis2, Danilo P Mandic3
1Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, U.K. yao.xu15@imperial.ac.uk.
本研究引入了图形规律化张量回归 (GRTR),以解决数据分析中的维数诅咒. GRTR有效地结合了领域知识,提高了模型的解释性和性能,同时降低了计算成本.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 应用数学 应用数学 应用数学
背景情况:
- 高维数据给传统的机器学习带来了挑战,原因是维度的诅咒.
- 张量分解 (TD) 提供了计算优势,但往往缺乏域知识集成.
- 现有的张量模型很难将先前的信息纳入模型压缩中.
研究的目的:
- 引入一个新的图形规则化张量回归 (GRTR) 框架.
- 将域知识集成到高维模型中,使用图形拉普拉斯规范化.
- 提高模型的解释性,减少数据分析中的计算复杂性.
主要方法:
- 开发了一个图形规则化的张量回归 (GRTR) 框架.
- 通过图形将域知识纳入拉普拉斯矩阵作为规范化术语.
- 利用张量代数来实现系数智能和维度智能模型的解释性.
主要成果:
- 在多路回归设置中,GRTR框架显示了更好的性能.
- 与竞争模型相比,实现了计算成本的显著降低.
- 验证了模型在模型参数中促进物理意义结构的能力.
结论:
- GRTR为分析高维数据提供了一种强大且可解释的方法.
- 该框架有效地平衡了模型压缩与域专业知识的整合.
- GRTR为计算密集型数据分析挑战提供了一个有前途的解决方案.
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