Bandingkan metode

	PCA Kernel	Analisis Komponen Utama
Bidang	Pembelajaran Mesin	Pembelajaran Mesin
Keluarga	Latent structure	Machine learning
Tahun asal	1998	2002
Pencetus	Schölkopf, B.; Smola, A. J.; Müller, K.-R.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Tipe	Nonlinear dimensionality reduction via kernel trick	Unsupervised dimensionality reduction
Sumber perintis	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.
Alias	KPCA, kernel PCA, nonlinear PCA via kernel trick, kernel eigenvalue decomposition	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Terkait	5	3
Ringkasan	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.