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Abstract: Kernel principal component analysis (KPCA), as a kernelized version of principal component analysis, is becoming a ubiquitous nonlinear method applied to various data analysis and processing tasks. (Update)
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BibTeX entry: (Update)
Z. Zhang. Probabilistic kernel principal component analysis. Technical report, Department of Computer Science, Hong Kong University of Science and Technology, 2004. http://citeseer.ist.psu.edu/zhang04probabilistic.html More
@misc{ zhang04probabilistic,
author = "Z. Zhang",
title = "Probabilistic kernel principal component analysis",
text = "Z. Zhang. Probabilistic kernel principal component analysis. Technical
report, Department of Computer Science, Hong Kong University of Science
and Technology, 2004.",
year = "2004",
url = "citeseer.ist.psu.edu/zhang04probabilistic.html" }
Citations (may not include all citations):218 Principal component analysis (context) - Jolli - 2002
187 Nonlinear component analysis as a kernel eigenvalue problem (context) - Scholkopf, Smola et al. - 1998
92 Probabilistic principal component analysis - Tipping, Bishop - 1999
88 Multidimensional Scaling (context) - Cox, Cox - 2000
15 Matrix Variate Distributions (context) - Gupta, Nagar - 2000
12 Analysis of multiphase flows using dual-energy gamma densito.. (context) - Bishop, James - 1993
7 Gaussian process latent variable models for visualisation of.. - Lawrence - 2004
6 a connection between kernel PCA and metric multidimensional .. - Williams - 2001
3 Singular Wishart and multivariate Beta distributions (context) - Srivastava - 2003
2 Wishart processes: A statistical view of reproducing kernels - Zhang, Yeung et al. - 2004
2 Learning metrics via discriminant kernels and multidimension.. (context) - Zhang - 2003
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