{"title":"主成分法的应用","authors":"Horia F. Pop, M. Frentiu","doi":"10.1109/CANS.2008.20","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":50026,"journal":{"name":"Journal of Systems Science & Complexity","volume":"27 1","pages":"103-109"},"PeriodicalIF":2.6000,"publicationDate":"2008-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Applications of Principal Components Methods\",\"authors\":\"Horia F. Pop, M. Frentiu\",\"doi\":\"10.1109/CANS.2008.20\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":50026,\"journal\":{\"name\":\"Journal of Systems Science & Complexity\",\"volume\":\"27 1\",\"pages\":\"103-109\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2008-11-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Systems Science & Complexity\",\"FirstCategoryId\":\"100\",\"ListUrlMain\":\"https://doi.org/10.1109/CANS.2008.20\",\"RegionNum\":3,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Systems Science & Complexity","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1109/CANS.2008.20","RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
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.
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.
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