主成分法的应用

IF 2.6 3区数学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Journal of Systems Science & Complexity Pub Date : 2008-11-08 DOI:10.1109/CANS.2008.20

Horia F. Pop, M. Frentiu

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引用次数: 0

摘要

在过去的几十年中，用于分析大量数据的多元统计方法已被应用于不同领域的问题解决。本文总结了主成分分析(PCA)方法及其鲁棒模糊替代方法的要点，并描述了一些突出该方法实际用途的应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Applications of Principal Components Methods

Multivariate statistical methods for the analysis of large quantities of data have been applied to problem solving in different domains during the last decades. This paper summarizes the main points of the principal components analysis (PCA) method and its robust fuzzy alternatives, and describes a few applications highlighting the practical usefulness of this approach.

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来源期刊

Journal of Systems Science & Complexity 数学-数学跨学科应用

CiteScore

3.80

自引率

9.50%

发文量

审稿时长

6-12 weeks

期刊介绍： The Journal of Systems Science and Complexity is dedicated to publishing high quality papers on mathematical theories, methodologies, and applications of systems science and complexity science. It encourages fundamental research into complex systems and complexity and fosters cross-disciplinary approaches to elucidate the common mathematical methods that arise in natural, artificial, and social systems. Topics covered are: complex systems, systems control, operations research for complex systems, economic and financial systems analysis, statistics and data science, computer mathematics, systems security, coding theory and crypto-systems, other topics related to systems science.