1. bookVolume 51 (2014): Issue 1 (June 2014)
Journal Details
License
Format
Journal
eISSN
2199-577X
First Published
17 Aug 2013
Publication timeframe
2 times per year
Languages
English
access type Open Access

A kernel-based learning algorithm combining kernel discriminant coordinates and kernel principal components

Published Online: 06 Jun 2014
Volume & Issue: Volume 51 (2014) - Issue 1 (June 2014)
Page range: 57 - 73
Journal Details
License
Format
Journal
eISSN
2199-577X
First Published
17 Aug 2013
Publication timeframe
2 times per year
Languages
English
SUMMARY

Kernel principal components (KPC) and kernel discriminant coordinates (KDC), which are the extensions of principal components and discriminant coordinates, respectively, from a linear domain to a nonlinear domain via the kernel trick, are two very popular nonlinear feature extraction methods. The kernel discriminant coordinates space has proven to be a very powerful space for pattern recognition. However, further study shows that there are still drawbacks in this method. To improve the performance of pattern recognition, we propose a new learning algorithm combining the advantages of KPC and KDC

Keywords

Aronszajn N. (1950): Theory of reproducing kernels. Transactions of the American Mathematical Society 68: 337-404.10.1090/S0002-9947-1950-0051437-7Search in Google Scholar

Badat G., Anouar F. (2000): Generalized discriminant analysis using a kernel approach. Neural Computation 12: 2385-2404.10.1162/08997660030001498011032039Search in Google Scholar

Bache K., Lichman M. (2013): UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.Search in Google Scholar

Fisher R.A. (1936): The use of multiple measurements in taxonomic problem. Annals of Eugenics 7: 179-188.10.1111/j.1469-1809.1936.tb02137.xSearch in Google Scholar

Friedman J.H. (1989): Regularized discriminant analysis. Journal of the American Statistical Association 84: 165-175.10.1080/01621459.1989.10478752Search in Google Scholar

Hotelling H. (1933): Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology 24: 417-441, 498-520.10.1037/h0070888Search in Google Scholar

Mika S., Rätsch G., Weston J., Schölkopf B., Müller K.R. (1999): Fisher discriminant analysis with kernels. In Y.H. Hu, J. Larsen, E. Wilson, and S. Douglas (eds.), Neural Networks for Signal Processing IV: 41-48.10.1109/NNSP.1999.788121Search in Google Scholar

Schölkopf B., Smola A., Müller K.B. (1998): Nonlinear component analysis as a kernel eigenvalues problem. Neural Computation 10: 1299-1319.10.1162/089976698300017467Search in Google Scholar

Seber G.A.F. (1984): Multivariate Observations. Wiley, New York.10.1002/9780470316641Search in Google Scholar

Shawe-Taylor J., Cristianini N. (2004): Kernel methods for pattern analysis. Cambridge University Press, Cambridge, UK.10.1017/CBO9780511809682Search in Google Scholar

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