Módszerek összehasonlítása

	Kernel PCA	Isomap
Tudományterület	Gépi tanulás	Gépi tanulás
Módszercsalád	Latent structure	Latent structure
Keletkezés éve	1998	2000
Megalkotó	Schölkopf, B.; Smola, A. J.; Müller, K.-R.	Tenenbaum, J. B.; de Silva, V.; Langford, J. C.
Típus	Nonlinear dimensionality reduction via kernel trick	Manifold learning / nonlinear dimensionality reduction
Alapmű	Schölkopf, B., Smola, A. J., & Müller, K.-R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5), 1299–1319.	Tenenbaum, J. B., de Silva, V. & Langford, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500), 2319–2323.
Alternatív nevek	KPCA, kernel PCA, nonlinear PCA via kernel trick, kernel eigenvalue decomposition	Isomap, isometric feature mapping, geodesic Isomap, nonlinear MDS
Kapcsolódó	5	3
Összefoglaló	Kernel Principal Component Analysis (Kernel PCA) is a nonlinear dimensionality-reduction method introduced by Bernhard Schölkopf, Alexander Smola, and Klaus-Robert Müller in 1997–1998. It extends classical linear PCA to curved, non-linear data manifolds by implicitly mapping input data into a high-dimensional feature space via a kernel function, then performing standard PCA in that space — all without ever computing the mapping explicitly.	Isomap (Isometric Feature Mapping) is a manifold learning algorithm introduced by Tenenbaum, de Silva, and Langford in 2000 that discovers the intrinsic low-dimensional geometry of high-dimensional data by preserving geodesic — rather than straight-line Euclidean — distances between all pairs of points. It was one of the earliest, and most influential, nonlinear dimensionality reduction methods to demonstrate that genuinely curved data manifolds could be unfolded into a faithful low-dimensional coordinate system.