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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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Related topics

Related topics

14 relations
Cluster analysisDeep learningGramian matrixKernel eigenvoice

Broader (1)

Signal processing

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2019
2019
Dimension Reduction and Kernel Principal Component Analysis
We study non-linear data-dimension reduction. We are motivated by the classical linear framework of Principal Component Analysis… 
2013
2013
Adaptive kernel principal component analysis for nonlinear dynamic process monitoring
In this paper a new algorithms for adaptive kernel principal component analysis (AKPCA) is proposed for dynamic process… 
2011
2011
A Robust Weighted Kernel Principal Component Analysis Algorithm
Kernel principal component analysis (KPCA) fails to detect the nonlinear structure of data well when outliers exist. To reduce… 
2007
2007
An efficient algorithm for Kernel two-dimensional principal component analysis
Recently, a new approach called two-dimensional principal component analysis (2DPCA) has been proposed for face representation… 
2007
2007
A Note on Robust Kernel Principal Component Analysis
Extending the classical principal component analysis (PCA), the kernel PCA (Schölkopf, Smola and Müller, 1998) effectively… 
2006
2006
Face Recognition with Weighted Kernel Principal Component Analysis
Principal component analysis (PCA) is one of the most traditional linear dimensionality reduction algorithms. Kernel principal… 
2006
2006
Feature extraction based on Kernel Principal Component Analysis
In order to confirm the advantage of Kernel Principal Component Analysis(KPCA) in feature extraction,six schemes were designed to… 
2004
2004
Kernel Principal Component Analysis and Application in Face Recognition
Eigenface or principal component analysis(PCA) as a method of feature extraction demonstrates their success in face recognition… 
2002
2002
Face recognition using kernel principal component analysis and genetic algorithms
Kernel principal component analysis (KPCA) as a powerful nonlinear feature extraction method has proven as a preprocessing step… 
2001
2001
Phoneme Classification Using Kernel Principal Component Analysis
A substantial number of linear and nonlinear feature space transformation methods have been proposed in recent years. Using the… 
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