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Arrow Diagrams for Kernel Principal Component Analysis
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 Title & Authors
Arrow Diagrams for Kernel Principal Component Analysis
Huh, Myung-Hoe;
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 Abstract
Kernel principal component analysis(PCA) maps observations in nonlinear feature space to a reduced dimensional plane of principal components. We do not need to specify the feature space explicitly because the procedure uses the kernel trick. In this paper, we propose a graphical scheme to represent variables in the kernel principal component analysis. In addition, we propose an index for individual variables to measure the importance in the principal component plane.
 Keywords
Principal component analysis;kernel method;radial basis function;biplot;arrow diagram;
 Language
English
 Cited by
1.
SVM-Guided Biplot of Observations and Variables,;

Communications for Statistical Applications and Methods, 2013. vol.20. 6, pp.491-498 crossref(new window)
2.
Global and Local Views of the Hilbert Space Associated to Gaussian Kernel,;

Communications for Statistical Applications and Methods, 2014. vol.21. 4, pp.317-325 crossref(new window)
1.
SVM-Guided Biplot of Observations and Variables, Communications for Statistical Applications and Methods, 2013, 20, 6, 491  crossref(new windwow)
2.
Global and Local Views of the Hilbert Space Associated to Gaussian Kernel, Communications for Statistical Applications and Methods, 2014, 21, 4, 317  crossref(new windwow)
 References
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2.
Hastie, T., Tibshirani, R. and Friedman, J. (2009). The Elements of Statistical Learning, Second Edition, Springer, New York.

3.
Karatzoglou, A., Smola, A., Hornik, K. and Zeileis, A. (2004). 'kernlab' - An S4 package for kernel methods in R, Journal of Statistical Software, 11, 1-20.

4.
Karatzoglou, A., Smola, A. and Hornik, K. (2012). R Package 'kernlab' (Version 0.9-15), http://cran.r-project.org/

5.
Scholkopf, B., Smola, A. and Muller, K. R. (1998). Nonlinear component analysis as a kernel eigenvalue problem, Neural Computation, 10, 1299-1319. crossref(new window)