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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 analysis
Deep learning
Gramian matrix
Kernel eigenvoice
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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
P. Jorgensen
,
Sooran Kang
,
Myung-Sin Song
,
Feng Tian
2019
Corpus ID: 189928071
We study non-linear data-dimension reduction. We are motivated by the classical linear framework of Principal Component Analysis…
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2013
2013
Adaptive kernel principal component analysis for nonlinear dynamic process monitoring
Chakour Chouaib
,
M. Harkat
,
Djeghaba Messaoud
Asian Control Conference
2013
Corpus ID: 3388667
In this paper a new algorithms for adaptive kernel principal component analysis (AKPCA) is proposed for dynamic process…
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2011
2011
A Robust Weighted Kernel Principal Component Analysis Algorithm
Xifa Duan
,
Z. Tian
,
Peiyan Qi
,
Xiangzeng Liu
International Conference of Information…
2011
Corpus ID: 18305281
Kernel principal component analysis (KPCA) fails to detect the nonlinear structure of data well when outliers exist. To reduce…
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2007
2007
An efficient algorithm for Kernel two-dimensional principal component analysis
Ning Sun
,
Haixian Wang
,
Zhen-hai Ji
,
C. Zou
,
Li Zhao
Neural computing & applications (Print)
2007
Corpus ID: 28722843
Recently, a new approach called two-dimensional principal component analysis (2DPCA) has been proposed for face representation…
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2007
2007
A Note on Robust Kernel Principal Component Analysis
Xinwei Deng
,
Ming Yuan
,
A. Sudjianto
2007
Corpus ID: 2576686
Extending the classical principal component analysis (PCA), the kernel PCA (Schölkopf, Smola and Müller, 1998) effectively…
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2006
2006
Face Recognition with Weighted Kernel Principal Component Analysis
Nan Liu
,
Han Wang
,
W. Yau
International Conference on Control, Automation…
2006
Corpus ID: 907250
Principal component analysis (PCA) is one of the most traditional linear dimensionality reduction algorithms. Kernel principal…
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2006
2006
Feature extraction based on Kernel Principal Component Analysis
Wei Zheng-zhong
2006
Corpus ID: 63642410
In order to confirm the advantage of Kernel Principal Component Analysis(KPCA) in feature extraction,six schemes were designed to…
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2004
2004
Kernel Principal Component Analysis and Application in Face Recognition
Shao Hui-he
2004
Corpus ID: 64295387
Eigenface or principal component analysis(PCA) as a method of feature extraction demonstrates their success in face recognition…
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2002
2002
Face recognition using kernel principal component analysis and genetic algorithms
Yankun Zhang
,
Chong-qing Liu
Proceedings of the 12th IEEE Workshop on Neural…
2002
Corpus ID: 122917193
Kernel principal component analysis (KPCA) as a powerful nonlinear feature extraction method has proven as a preprocessing step…
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2001
2001
Phoneme Classification Using Kernel Principal Component Analysis
A. Kocsor
,
András Kuba
,
L. Tóth
2001
Corpus ID: 12856163
A substantial number of linear and nonlinear feature space transformation methods have been proposed in recent years. Using the…
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