Data analytics using canonical correlation analysis and Monte Carlo simulation
作者机构：Department of PhysicsLehigh UniversityBethlehemPA 18015USA Department of Materials Science and EngineeringLehigh UniversityBethlehemPA 18015USA Department of Materials Science and EngineeringCarnegie Mellon UniversityPittsburghPA 15213USA Almatis Inc.LeetsdalePA 15056USA
出 版 物：《npj Computational Materials》 (计算材料学（英文）)
年 卷 期：2017年第3卷第1期
页 面：234-239页
核心收录：
学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学]
基 金：support from the Office of Naval Research under grant N00014-11-1-0678
主 题：canonical analytic absorber
摘 要：A canonical correlation analysis is a generic parametric model used in the statistical analysis of data involving interrelated or interdependent input and output *** is especially useful in data analytics as a dimensional reduction strategy that simplifies a complex,multidimensional parameter space by identifying a relatively few combinations of variables that are maximally *** shortcoming of the canonical correlation analysis,however,is that it provides only a linear combination of variables that maximizes these *** this in mind,we describe here a versatile,Monte-Carlo based methodology that is useful in identifying non-linear functions of the variables that lead to strong input/output *** demonstrate that our approach leads to a substantial enhancement of correlations,as illustrated by two experimental applications of substantial interest to the materials science community,namely:(1)determining the interdependence of processing and microstructural variables associated with doped polycrystalline aluminas,and(2)relating microstructural decriptors to the electrical and optoelectronic properties of thin-film solar cells based on CuInSe_(2) ***,we describe how this approach facilitates experimental planning and process control.
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