Compara mètodes

	Kernel PCA	Màquina de Vectors de Suport (Classificació)
Camp	Aprenentatge automàtic	Aprenentatge automàtic
Família	Latent structure	Machine learning
Any d'origen	1998	1995
Autor original	Schölkopf, B.; Smola, A. J.; Müller, K.-R.	Cortes, C. & Vapnik, V.
Tipus	Nonlinear dimensionality reduction via kernel trick	Maximum-margin classifier (kernel method)
Font seminal	Schölkopf, B., Smola, A. J., & Müller, K.-R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5), 1299–1319.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.
Àlies	KPCA, kernel PCA, nonlinear PCA via kernel trick, kernel eigenvalue decomposition	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier
Relacionats	5	5
Resum	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.	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.