Comparer des méthodes

	Autoencodeur	Analyse en composantes principales
Domaine	Apprentissage profond	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2006	2002
Auteur d'origine	Hinton, G.E. & Salakhutdinov, R.R.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Type	Neural network (encoder-decoder)	Unsupervised dimensionality reduction
Source fondatrice	Hinton, G.E. & Salakhutdinov, R.R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Alias	Otokodlayıcı (Autoencoder), otokodlayıcı, auto-encoder, encoder-decoder network	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Apparentées	4	3
Résumé	An autoencoder is an encoder-decoder neural network, popularised by Hinton and Salakhutdinov in 2006, that compresses data into a low-dimensional latent code and then reconstructs it, enabling dimensionality reduction and anomaly detection. By learning to rebuild its own input through a narrow bottleneck, it discovers a compact representation of the data.	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.