قارن الطرق

	التضمين الخطي المحلي (LLE)	تحليل المكونات الرئيسية باستخدام النواة (Kernel PCA)
المجال	تعلم الآلة	تعلم الآلة
العائلة	Machine learning	Latent structure
سنة النشأة	2000	1998
صاحب الطريقة	Sam Roweis & Lawrence Saul	Schölkopf, B.; Smola, A. J.; Müller, K.-R.
النوع	Nonlinear manifold dimensionality reduction	Nonlinear dimensionality reduction via kernel trick
المصدر التأسيسي	Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500), 2323–2326.	Schölkopf, B., Smola, A. J., & Müller, K.-R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5), 1299–1319.
الأسماء البديلة	LLE, manifold learning, nonlinear dimensionality reduction, yerel doğrusal gömme	KPCA, kernel PCA, nonlinear PCA via kernel trick, kernel eigenvalue decomposition
ذات صلة	3	5
الملخص	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.	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.