{"title":"生物特征数据中的异常值-分析的两个真实例子","authors":"A. Bartkowiak","doi":"10.1109/ICBAKE.2009.47","DOIUrl":null,"url":null,"abstract":"The problem of finding and identifying outliers in multivariate measurement data is as old as the data analysis itself. The question: \"what is an outlier\" has many answers, is is especially difficult when considering high-dimensional data. In the paper a search for outliers in two real data sets is shown. It is stressed that identifying outliers should not be done on the basis of asymptotical cutoffs derived under assumption of normality of the analyzed data.","PeriodicalId":137627,"journal":{"name":"2009 International Conference on Biometrics and Kansei Engineering","volume":"55 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Outliers in Biometrical Data - Two Real Examples of Analysis\",\"authors\":\"A. Bartkowiak\",\"doi\":\"10.1109/ICBAKE.2009.47\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The problem of finding and identifying outliers in multivariate measurement data is as old as the data analysis itself. The question: \\\"what is an outlier\\\" has many answers, is is especially difficult when considering high-dimensional data. In the paper a search for outliers in two real data sets is shown. It is stressed that identifying outliers should not be done on the basis of asymptotical cutoffs derived under assumption of normality of the analyzed data.\",\"PeriodicalId\":137627,\"journal\":{\"name\":\"2009 International Conference on Biometrics and Kansei Engineering\",\"volume\":\"55 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-06-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 International Conference on Biometrics and Kansei Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICBAKE.2009.47\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 International Conference on Biometrics and Kansei Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICBAKE.2009.47","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Outliers in Biometrical Data - Two Real Examples of Analysis
The problem of finding and identifying outliers in multivariate measurement data is as old as the data analysis itself. The question: "what is an outlier" has many answers, is is especially difficult when considering high-dimensional data. In the paper a search for outliers in two real data sets is shown. It is stressed that identifying outliers should not be done on the basis of asymptotical cutoffs derived under assumption of normality of the analyzed data.
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