השוואת שיטות

	PCA באמצעות גרעין (Kernel PCA)	Locally Linear Embedding (LLE)
תחום	למידת מכונה	למידת מכונה
משפחה	Latent structure	Machine learning
שנת המקור	1998	2000
הוגה השיטה	Schölkopf, B.; Smola, A. J.; Müller, K.-R.	Sam Roweis & Lawrence Saul
סוג	Nonlinear dimensionality reduction via kernel trick	Nonlinear manifold dimensionality reduction
מקור מכונן	Schölkopf, B., Smola, A. J., & Müller, K.-R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5), 1299–1319.	Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500), 2323–2326.
כינויים	KPCA, kernel PCA, nonlinear PCA via kernel trick, kernel eigenvalue decomposition	LLE, manifold learning, nonlinear dimensionality reduction, yerel doğrusal gömme
קשורות	5	3
תקציר	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.	Locally linear embedding, introduced by Sam Roweis and Lawrence Saul in 2000, is a manifold-learning method for nonlinear dimensionality reduction. It assumes that although data may curve through a high-dimensional space, each point and its neighbours lie approximately on a flat patch. LLE captures each point as a weighted combination of its neighbours and then finds a low-dimensional layout that preserves those same local relationships, unrolling curved structure into a faithful low-dimensional map.