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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… 
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Papers overview

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2019
2019
We study non-linear data-dimension reduction. We are motivated by the classical linear framework of Principal Component Analysis… 
2013
2013
In this paper a new algorithms for adaptive kernel principal component analysis (AKPCA) is proposed for dynamic process… 
2011
2011
Kernel principal component analysis (KPCA) fails to detect the nonlinear structure of data well when outliers exist. To reduce… 
2007
2007
Recently, a new approach called two-dimensional principal component analysis (2DPCA) has been proposed for face representation… 
2007
2007
Extending the classical principal component analysis (PCA), the kernel PCA (Schölkopf, Smola and Müller, 1998) effectively… 
2006
2006
Principal component analysis (PCA) is one of the most traditional linear dimensionality reduction algorithms. Kernel principal… 
2006
2006
In order to confirm the advantage of Kernel Principal Component Analysis(KPCA) in feature extraction,six schemes were designed to… 
2004
2004
Eigenface or principal component analysis(PCA) as a method of feature extraction demonstrates their success in face recognition… 
2002
2002
Kernel principal component analysis (KPCA) as a powerful nonlinear feature extraction method has proven as a preprocessing step… 
2001
2001
A substantial number of linear and nonlinear feature space transformation methods have been proposed in recent years. Using the…