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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Cluster analysisDeep learningGramian matrixKernel eigenvoice
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Signal processing

Papers overview

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Highly Cited
2016
Highly Cited
2016
Nonlinear process monitoring using improved kernel principal component analysis
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
Distributed Kernel Principal Component Analysis
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
Robust kernel principal component analysis and classification
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
Robust Kernel Principal Component Analysis
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
Robust Kernel Principal Component Analysis
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
Incremental Kernel Principal Component Analysis
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
Statistical properties of kernel principal component analysis
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
Iterative kernel principal component analysis for image modeling
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
Sparse Kernel Principal Component Analysis
'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
Kernel Principal Component Analysis
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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