Comparer des méthodes

	Isomap	Plongement Linéaire Local (LLE)
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Latent structure	Machine learning
Année d'origine	2000	2000
Auteur d'origine	Tenenbaum, J. B.; de Silva, V.; Langford, J. C.	Sam Roweis & Lawrence Saul
Type	Manifold learning / nonlinear dimensionality reduction	Nonlinear manifold dimensionality reduction
Source fondatrice	Tenenbaum, J. B., de Silva, V. & Langford, J. C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500), 2319–2323.	Roweis, S. T., & Saul, L. K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500), 2323–2326.
Alias	Isomap, isometric feature mapping, geodesic Isomap, nonlinear MDS	LLE, manifold learning, nonlinear dimensionality reduction, yerel doğrusal gömme
Apparentées	3	3
Résumé	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.	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.