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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.
2013
2013
Non-linear and Sparse Representations for Multi-Modal Recognition
H. Nguyen
2013
Corpus ID: 32085337
Title of dissertation: NON-LINEAR AND SPARSE REPRESENTATIONS FOR MULTI-MODAL RECOGNITION Hien Van Nguyen, Doctor of Philosophy…
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2012
2012
Nonlinear process monitoring using wavelet kernel principal component analysis
Ke Guo
,
Ye San
,
Yi Zhu
International Conference on Systems and…
2012
Corpus ID: 14327879
Conventional principal component analysis (PCA) performs poorly in nonlinear process monitoring due to it only can capture the…
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2011
2011
Nonlinear Robust Regression Using Kernel Principal Component Analysis and R-Estimators
Antoni Wibowo
,
M. I. Desa
2011
Corpus ID: 16299268
In recent years, many algorithms based on kernel principal component analysis (KPCA) have been proposed including kernel…
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2011
2011
Explicit signal to noise ratio in reproducing kernel Hilbert spaces
L. Gómez-Chova
,
A. Nielsen
,
Gustau Camps-Valls
IEEE International Geoscience and Remote Sensing…
2011
Corpus ID: 9519095
This paper introduces a nonlinear feature extraction method based on kernels for remote sensing data analysis. The proposed…
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2011
2011
Principal component analysis and kernel principal component analysis based on cosine angle distance
Jin Zhon
2011
Corpus ID: 124659840
Principal Component Analysis(PCA) and Kernel Principal Component Analysis(KPCA) are both proposed based on Euclidean distance…
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2010
2010
Error bounds for suboptimal solutions to kernel principal component analysis
G. Gnecco
,
M. Sanguineti
Optimization Letters
2010
Corpus ID: 29132551
Suboptimal solutions to kernel principal component analysis are considered. Such solutions take on the form of linear…
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2010
2010
Kernel Principal Component Analysis based on Geodesic Distance
Ning Xue
2010
Corpus ID: 125063503
Feature extraction,which is a key step in data analysis such as classification,clustering and so on,has an important impact on…
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2006
2006
3D Object Pose Inference via Kernel Principal Component Analysis with Image Euclidian Distance (IMED)
T. Tangkuampien
,
D. Suter
British Machine Vision Conference
2006
Corpus ID: 8017977
Kernel Principal Component Analysis (KPCA) is a powerful non-linear unsupervised learning technique for high dimensional pattern…
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2006
2006
Sensitivity Analysis in Kernel Principal Component Analysis
Yoshihiro Yamanishi
,
Y. Tanaka
2006
Corpus ID: 13714654
In this paper we derive empirical influence functions for features in kernel principal component analysis. Based on the derived…
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2003
2003
NONLINEAR DATA RECONCILIATION METHOD BASED ON KERNEL PRINCIPAL COMPONENT ANALYSIS
YanWeiwu
,
ShaoHuihe
2003
Corpus ID: 60980678
In the industrial process situation, principal component analysis (PCA) is a general method in data reconciliation.However, PCA…
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