Sammenlign metoder

	Kernel PCA	Principal Component Analysis
Fagområde	Maskinlæring	Maskinlæring
Familie	Latent structure	Machine learning
Oprindelsesår	1998	2002
Ophavsperson	Schölkopf, B.; Smola, A. J.; Müller, K.-R.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Type	Nonlinear dimensionality reduction via kernel trick	Unsupervised dimensionality reduction
Oprindelig kilde	Schölkopf, B., Smola, A. J., & Müller, K.-R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5), 1299–1319.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Aliasser	KPCA, kernel PCA, nonlinear PCA via kernel trick, kernel eigenvalue decomposition	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Relaterede	5	3
Resumé	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.	Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.