Comparar métodos

	PCA con kernel	Isomap	Máquina de Vectores de Soporte (Clasificación)
Campo	Aprendizaje automático	Aprendizaje automático	Aprendizaje automático
Familia	Latent structure	Latent structure	Machine learning
Año de origen	1998	2000	1995
Autor original	Schölkopf, B.; Smola, A. J.; Müller, K.-R.	Tenenbaum, J. B.; de Silva, V.; Langford, J. C.	Cortes, C. & Vapnik, V.
Tipo	Nonlinear dimensionality reduction via kernel trick	Manifold learning / nonlinear dimensionality reduction	Maximum-margin classifier (kernel method)
Fuente seminal	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.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
Alias	KPCA, kernel PCA, nonlinear PCA via kernel trick, kernel eigenvalue decomposition	Isomap, isometric feature mapping, geodesic Isomap, nonlinear MDS	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
Relacionados	5	3	5
Resumen	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.	The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data.