Comparer des méthodes

	Analyse en composantes principales	t-SNE
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2002	2008
Auteur d'origine	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	van der Maaten, L. & Hinton, G.
Type	Unsupervised dimensionality reduction	Nonlinear dimensionality reduction (manifold visualization)
Source fondatrice	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.	van der Maaten, L. & Hinton, G. (2008). Visualizing Data using t-SNE. Journal of Machine Learning Research, 9(86), 2579–2605.
Alias	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform	t-SNE (Boyut İndirgeme / Görselleştirme), t-distributed stochastic neighbor embedding, tsne
Apparentées	3	3
Résumé	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.	t-SNE (t-Distributed Stochastic Neighbor Embedding) is a nonlinear dimensionality-reduction method introduced by Laurens van der Maaten and Geoffrey Hinton in 2008 that maps high-dimensional data into a 2D or 3D space for visualization. It preserves probabilistic local similarities, so points that are neighbours in the original space stay close together, revealing cluster structure and local neighbourhoods.