Comparer des méthodes

	Isomap	Analyse en composantes principales
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Latent structure	Machine learning
Année d'origine	2000	2002
Auteur d'origine	Tenenbaum, J. B.; de Silva, V.; Langford, J. C.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Type	Manifold learning / nonlinear dimensionality reduction	Unsupervised 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.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Alias	Isomap, isometric feature mapping, geodesic Isomap, nonlinear MDS	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
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.	Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.