About: Kernel principal component analysis

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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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rdfs:label	Kernel principal component analysis (en) 核主成分分析 (zh)
rdfs:comment	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. (en) 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。 (zh)
foaf:depiction
dcterms:subject	Kernel methods for machine learning Machine learning algorithms Signal processing Dimension reduction
Wikipage page ID	5978424 (xsd:integer)
Wikipage revision ID	1056934237 (xsd:integer)
Link from a Wikipage to another Wikipage	Kernel methods for machine learning Multivariate statistics Kernel methods Bergman kernel Eigendecomposition of a matrix Gramian matrix Cluster analysis Machine learning algorithms Signal processing Linear separability Centering matrix Principal component analysis Covariance matrix Hyperplane Dimension reduction Kernel trick Nonlinear dimensionality reduction Spectral clustering Almost always
sameAs	Kernel principal component analysis Kernel principal component analysis Kernel principal component analysis Kernel principal component analysis Kernel principal component analysis
dbp:wikiPageUsesTemplate	dbt:Reflist
thumbnail	wiki-commons:Special:FilePath/Kernel_pca_input.png?width=300
Link from a Wikipa... related subject.	Principal component analysis
has abstract	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. (en) 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。 (zh)
gold:hypernym	Extension
prov:wasDerivedFrom	wikipedia-en:Kernel_principal_component_analysis?oldid=1056934237&ns=0
page length (characters) of wiki page	8545 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:Kernel_principal_component_analysis
is Link from a Wikipage to another Wikipage of	Ensemble learning Semidefinite embedding Mlpack Kernel Fisher discriminant analysis Kernel embedding of distributions Kernel regression Pattern recognition Dimensionality reduction Gram matrix Principal component analysis Kernel eigenvoice Component analysis List of statistics articles KPCA Nonlinear dimensionality reduction Outline of machine learning Types of artificial neural networks Spectral clustering Yuichi Motai Kernel PCA
is Wikipage redirect of	Kernel PCA
is Wikipage disambiguates of	Component analysis KPCA
is foaf:primaryTopic of	wikipedia-en:Kernel_principal_component_analysis

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