前向变量选择能够快速准确地识别使用卡鲁宁-洛埃夫分解高斯过程的动态系统.
Kyle Hayes1,2, Michael W Fouts2, Ali Baheri2
1National Energy Technology Laboratory, Morgantown, WV, United States of America.
PloS one
|September 20, 2024
概括
本研究介绍了一种新的可变选择方法,用于使用卡鲁宁-洛耶夫 (KL) 分解的可扩展高斯过程 (GPs). 该方法有效地识别了关键术语,为动态系统识别提供了具有竞争力的准确性和速度.
科学领域:
- 机器学习 机器学习
- 计算统计学 计算统计学
- 动态系统建模 动态系统建模
背景情况:
- 可扩展的高斯过程 (GPs) 对大型数据集至关重要.
- 卡鲁宁-洛埃夫 (KL) 分解为GP可扩展性提供了一个有希望的,无诱导点的方法.
- 由KL分解产生的高维度需要有效的变量选择.
研究的目的:
- 为KL分解的全科医生开发一种新的前变量选择方法.
- 为了使动态系统的高效和准确的建模.
- 为了减少GP培训和推理中的计算复杂性.
主要方法:
- 利用贝叶斯平滑分线ANOVA (BSS-ANOVA) 内核的KL扩展的有序基础函数.
- 在完全贝叶斯框架内实施快速吉布斯采样.
- 将该方法应用于通过建模触点空间动态来识别动态系统.
主要成果:
- 在表式数据集上实现了竞争力的准确性,并减少了训练/推理时间.
- 在"易受感染,感染,恢复" (SIR) 和"级联坦克"数据集上证明有效.
- 展示了适用于动态系统识别任务的适用性.
结论:
- 拟议的变量选择方法有效地限制了KL扩展的GP中的术语.
- 该方法为动态系统识别提供了计算效率高,准确的解决方案.
- 与随机森林,ResNets和SINDy相比,该方法在特定任务中显示出前景.
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