Krahasoni metodat

	Locally Linear Embedding (LLE)	PCA me bërthamë
Fusha	Mësimi i makinës	Mësimi i makinës
Familja	Machine learning	Latent structure
Viti i origjinës	2000	1998
Krijuesi	Sam Roweis & Lawrence Saul	Schölkopf, B.; Smola, A. J.; Müller, K.-R.
Lloji	Nonlinear manifold dimensionality reduction	Nonlinear dimensionality reduction via kernel trick
Burimi themelues	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.
Emërtime të tjera	LLE, manifold learning, nonlinear dimensionality reduction, yerel doğrusal gömme	KPCA, kernel PCA, nonlinear PCA via kernel trick, kernel eigenvalue decomposition
Të lidhura	3	5
Përmbledhja	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.