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Kernel principal component analysis 核主成分分析
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核主成分分析(英語:kernel principal component analysis,简称kernel PCA)是多变量统计领域中的一种分析方法,是使用对主成分分析的非线性扩展,即将原数据通过核映射到后再使用原本线性的主成分分析。 In the field of multivariate statistics, kernel principal component analysis (kernel PCA)is an extension of principal component analysis (PCA) using techniques of kernel methods. Using a kernel, the originally linear operations of PCA are performed in a reproducing kernel Hilbert space.
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核主成分分析(英語:kernel principal component analysis,简称kernel PCA)是多变量统计领域中的一种分析方法,是使用对主成分分析的非线性扩展,即将原数据通过核映射到后再使用原本线性的主成分分析。 In the field of multivariate statistics, kernel principal component analysis (kernel PCA)is an extension of principal component analysis (PCA) using techniques of kernel methods. Using a kernel, the originally linear operations of PCA are performed in a reproducing kernel Hilbert space.
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