Krahasoni metodat

	Isomap	PCA me bërthamë
Fusha	Mësimi i makinës	Mësimi i makinës
Familja	Latent structure	Latent structure
Viti i origjinës	2000	1998
Krijuesi	Tenenbaum, J. B.; de Silva, V.; Langford, J. C.	Schölkopf, B.; Smola, A. J.; Müller, K.-R.
Lloji	Manifold learning / nonlinear dimensionality reduction	Nonlinear dimensionality reduction via kernel trick
Burimi themelues	Tenenbaum, J. B., de Silva, V. & Langford, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500), 2319–2323.	Schölkopf, B., Smola, A. J., & Müller, K.-R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5), 1299–1319.
Emërtime të tjera	Isomap, isometric feature mapping, geodesic Isomap, nonlinear MDS	KPCA, kernel PCA, nonlinear PCA via kernel trick, kernel eigenvalue decomposition
Të lidhura	3	5
Përmbledhja	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.	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.