Krahasoni metodat

	Locally Linear Embedding (LLE)	Isomap
Fusha	Mësimi i makinës	Mësimi i makinës
Familja	Machine learning	Latent structure
Viti i origjinës	2000	2000
Krijuesi	Sam Roweis & Lawrence Saul	Tenenbaum, J. B.; de Silva, V.; Langford, J. C.
Lloji	Nonlinear manifold dimensionality reduction	Manifold learning / nonlinear dimensionality reduction
Burimi themelues	Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500), 2323–2326.	Tenenbaum, J. B., de Silva, V. & Langford, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500), 2319–2323.
Emërtime të tjera	LLE, manifold learning, nonlinear dimensionality reduction, yerel doğrusal gömme	Isomap, isometric feature mapping, geodesic Isomap, nonlinear MDS
Të lidhura	3	3
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.	Isomap (Isometric Feature Mapping) is a manifold learning algorithm introduced by Tenenbaum, de Silva, and Langford in 2000 that discovers the intrinsic low-dimensional geometry of high-dimensional data by preserving geodesic — rather than straight-line Euclidean — distances between all pairs of points. It was one of the earliest, and most influential, nonlinear dimensionality reduction methods to demonstrate that genuinely curved data manifolds could be unfolded into a faithful low-dimensional coordinate system.