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dc.contributor.author游文杰
dc.contributor.author吉国力
dc.contributor.author袁明顺
dc.date.accessioned2016-05-17T02:42:44Z
dc.date.available2016-05-17T02:42:44Z
dc.date.issued2009
dc.identifier.citation计算机工程与应用,2009,(36):169-173
dc.identifier.issn1002-8331
dc.identifier.otherJSGG200936051
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/105115
dc.description.abstract针对一类高维少样本数据的特点,给出了广义小样本概念,对广义小样本进行信息特征压缩:特征提取(降维)和特征选择(选维)。首先介绍基于主成分分析(PCA)的无监督与基于偏最小二乘(PlS)的有监督的特征提取方法;其次通过分析第一成分结构,提出基于PCA与PlS的新的全局特征选择方法,并进一步提出基于PlS的递归特征排除法(PlS-rfE);最后针对MITAMl/All的分类问题,实现基于PCA与PlS的特征选择和特征提取,以及PlS-rfE特征选择与比较,达到广义小样本信息特征压缩的目的。
dc.description.abstractIn view of the characteristics of small sample and high dimensional data,Generalized Small Samples(GSS) is defined.It reduces information feature of GSS:feature extraction(dimensionality extraction) and feature selection(dimensionality selection).Firstly,unsupervised feature extraction based on Principal Component Analysis(PCA) and supervised feature extraction based on Partial Least Squares(PLS) are introduced.Secondly,analyzing the structure of first PC,it presents new global PCA-based and PLSbased feature selection approaches,in addition recursive feature elimination on PLS(PLS-RFE) is realized.Finally,the approaches are applied to the classification of MIT AML/ALL,it performs feature extraction on PCA and PLS,and feature selection compared with PLS-RFE.The information compression of GSS is realized.
dc.description.sponsorship高校博士点专项科研基金(No20070384003);福建省教育厅科技项目(NoJB08244)
dc.language.isozh_CN
dc.subject广义小样本
dc.subject主成分分析(PCA)
dc.subject偏最小二乘(PLS)
dc.subject特征提取
dc.subject特征选择
dc.subjectgeneralized small sample
dc.subjectPrincipal Component Analysis(PCA)
dc.subjectPartial Least Squares(PLS)
dc.subjectfeature extraction
dc.subjectfeature selection
dc.title高维少样本数据的特征压缩
dc.title.alternativeFeature reduction on high-dimensional small-sample data
dc.typeArticle


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