Kernel principal component analysis

Known as: Component analysis, KPCA, Kernel PCA 
In the field of multivariate statistics, kernel principal component analysis (kernel PCA) is an extension of principal component analysis (PCA) using… (More)
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Highly Cited
2016
Highly Cited
2016
Kernel principal component analysis (KPCA) has become a popular technique for process monitoring in recent years. However, the… (More)
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2015
2015
Kernel Principal Component Analysis (KPCA) is a key technique in machine learning for extracting the nonlinear structure of data… (More)
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2010
2010
Kernel principal component analysis (KPCA) extends linear PCA from a real vector space to any high dimensional kernel feature… (More)
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2009
2009
This letter discusses the robustness issue of kernel principal component analysis. A class of new robust procedures is proposed… (More)
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2008
2008
Kernel Principal Component Analysis (KPCA) is a popular gen eralization of linear PCA that allows non-linear feature extraction… (More)
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Highly Cited
2007
Highly Cited
2007
The kernel principal component analysis (KPCA) has been applied in numerous image-related machine learning applications and it… (More)
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2006
2006
The main goal of this paper is to prove inequalities on the reconstruction error for kernel principal component analysis. With… (More)
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Highly Cited
2005
Highly Cited
2005
In recent years, kernel principal component analysis (KPCA) has been suggested for various image processing tasks requiring an… (More)
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Highly Cited
2000
Highly Cited
2000
'Kernel' principal component analysis (PCA) is an elegant nonlinear generalisation of the popular linear data analysis method… (More)
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Highly Cited
1997
Highly Cited
1997
A new method for performing a nonlinear form of Principal Component Analysis is proposed. By the use of integral operator kernel… (More)
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