Kernel PCA
Kernel Principal Component Analysis (Kernel PCA) je nelinearna metoda redukcije dimenzionalnosti koju su uveli Bernhard Schölkopf, Alexander Smola i Klaus-Robert Müller 1997–1998. Ona proširuje klasičnu linearnu PCA na zakrivljene, nelinearne podatkovne manofolde implicitnim mapiranjem ulaznih podataka u prostor viših karakteristika putem kernel funkcije, a zatim vrši standardnu PCA u tom prostoru — sve bez eksplicitnog izračunavanja mapiranja.
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Izvori
- Schölkopf, B., Smola, A. J., & Müller, K.-R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5), 1299–1319. DOI: 10.1162/089976698300017467 ↗
- Schölkopf, B., Smola, A. J., & Müller, K.-R. (1997). Kernel principal component analysis. In Artificial Neural Networks — ICANN'97, Lecture Notes in Computer Science, Vol. 1327, pp. 583–588. Springer. DOI: 10.1007/BFb0020217 ↗
- Schölkopf, B., & Smola, A. J. (2002). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press. ISBN: 978-0-262-19475-4
Kako citirati ovu stranicu
ScholarGate. (2026, June 3). Kernel Principal Component Analysis. ScholarGate. https://scholargate.app/sr/machine-learning/kernel-pca
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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