在拓数据分析中,通过组等价非扩展型运算符对冲性降噪的概率结果
Patrizio Frosini1, Ivan Gridelli1, Andrea Pascucci1
1Department of Mathematics, University of Bologna, 40126 Bologna, Italy.
Entropy (Basel, Switzerland)
|August 26, 2023
概括
组等价非扩展运算符 (GENEO) 现在可以消除冲动噪声,并通过噪声数据稳定拓数据分析 (TDA). 在L-Lipschitz函数中,GENEOs控制来自均冲动噪声的持久性图干扰.
科学领域:
- 数学 数学 是一个数学.
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 在拓数据分析 (TDA) 和机器学习中,越来越多地使用组等价非扩展运算符 (GENEOs).
- 冲动性噪音和数据干扰对TDA稳定性和准确性构成挑战.
研究的目的:
- 调查GENEO在TDA中的冲动消除噪声的应用.
- 为了证明GENEO如何在处理杂数据时可以提高TDA的稳定性.
- 分析GENEO对持久性图干扰的影响,这种干扰是由均的冲动噪声引起的.
主要方法:
- 应用到L-Lipschitz函数的GENEO的理论分析.
- 噪声模型的数学表述及其对持久图的影响.
- 证明GENEO能够控制预期扰动值的能力.
主要成果:
- GENEO 有效地从数据中去除冲动噪声.
- 应用GENEO显著增加了TDA在有噪音数据的情况下的稳定性.
- GENEO提供了对均分布的冲动噪声引起的持久性图干扰预期值的控制.
结论:
- 在杂的环境中,GENEO提供了一种新的方法来实现强大的TDA.
- 这些发现将GENEO的适用性扩展到其最初的领域之外.
- 这项研究弥合了操作者理论和实际数据分析挑战之间的差距.
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