Topological data analysis: Concepts, computation, and applications in chemical engineering
Topological data analysis: Concepts, computation, and applications in chemical engineering
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拓扑数据分析:化学工程中的概念、计算和应用
DOI:
10.1016/j.compchemeng.2020.107202
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发表时间:
2021
影响因子:
4.3
通讯作者:
Zavala, Victor M.
中科院分区:
文献类型:
--
作者:
Smith, Alexander D.;Dłotko, Paweł;Zavala, Victor M.
A primary hypothesis that drives scientific and engineering studies is that data has structure. The dominant paradigms for describing such structure are statistics (e.g., moments, correlation functions) and signal processing (e.g., convolutional neural nets, Fourier series). Topological Data Analysis (TDA) is a field of mathematics that analyzes data from a fundamentally different perspective. TDA represents datasets as geometric objects and provides dimensionality reduction techniques that project such objects onto low-dimensional descriptors. The key properties of these descriptors (also known as topological features) are that they provide multiscale information and that they are stable under perturbations (e.g., noise, translation, and rotation). In this work, we review the key mathematical concepts and methods of TDA and present different applications in chemical engineering.
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DOI:
--
发表时间:
2015-07
期刊:
J. Mach. Learn. Res.
影响因子:
--
作者:
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3
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作者:
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