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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.
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… 
2010
2010
Automatic Gait Recognition Using Kernel Principal Component Analysis
Gait is one of the biometric technologies which can be identified as an individual by his/her walking style. This paper proposes… 
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
Greedy Kernel Principal Component Analysis
This contribution discusses one aspect of statistical learning and generalization. The theory of learning is very relevant to… 
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
Probabilistic Kernel Principal Component Analysis Through Time
This paper introduces a temporal version of Probabilistic Kernel Principal Component Analysis by using a hidden Markov model in… 
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… 
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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