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2014 | 51 | 1 | 57-73
Tytuł artykułu

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

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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
Wydawca
Czasopismo
Rocznik
Tom
51
Numer
1
Strony
57-73
Opis fizyczny
Daty
wydano
2014-06-01
online
2014-06-06
Twórcy
  • President Stanislaw Wojciechowski Higher Vocational State School in Kalisz, Institute of Management, Nowy Świat 4, 62-800 Kalisz, Poland, k.deregowski@pwsz.kalisz.pl
  • President Stanislaw Wojciechowski Higher Vocational State School in Kalisz, Institute of Management, Nowy Świat 4, 62-800 Kalisz, Poland, mkrzysko@amu.edu.pl
  • Adam Mickiewicz University, Faculty of Mathematics and Computer Science, Umultowska 87, 61-614 Poznań, Poland
Bibliografia
  • Aronszajn N. (1950): Theory of reproducing kernels. Transactions of the American Mathematical Society 68: 337-404.
  • Badat G., Anouar F. (2000): Generalized discriminant analysis using a kernel approach. Neural Computation 12: 2385-2404.
  • 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.
  • Fisher R.A. (1936): The use of multiple measurements in taxonomic problem. Annals of Eugenics 7: 179-188.
  • Friedman J.H. (1989): Regularized discriminant analysis. Journal of the American Statistical Association 84: 165-175.
  • Hotelling H. (1933): Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology 24: 417-441, 498-520.
  • 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.
  • Schölkopf B., Smola A., Müller K.B. (1998): Nonlinear component analysis as a kernel eigenvalues problem. Neural Computation 10: 1299-1319.[WoS]
  • Seber G.A.F. (1984): Multivariate Observations. Wiley, New York.
  • Shawe-Taylor J., Cristianini N. (2004): Kernel methods for pattern analysis. Cambridge University Press, Cambridge, UK.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.doi-10_2478_bile-2014-0005
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