Comparer des méthodes

	Machine à vecteurs de support auto-supervisée	ACP à noyau
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Latent structure
Année d'origine	2019–2021	1998
Auteur d'origine	Various (integration of self-supervised learning with SVM classifiers, ~2019–2021)	Schölkopf, B.; Smola, A. J.; Müller, K.-R.
Type	Hybrid (self-supervised pretraining + SVM classifier)	Nonlinear dimensionality reduction via kernel trick
Source fondatrice	De Palma, A., Bucarelli, M. S., Goyal, P., & Silvestri, F. (2021). Self-supervised Support Vector Machine. Proceedings of the AAAI Workshop on Self-Supervised Learning for the Internet of Things.	Schölkopf, B., Smola, A. J., & Müller, K.-R. (1998). Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, 10(5), 1299–1319.
Alias	Self-supervised SVM, SS-SVM, semi-self-supervised SVM, self-supervised kernel SVM	KPCA, kernel PCA, nonlinear PCA via kernel trick, kernel eigenvalue decomposition
Apparentées	5	5
Résumé	A Self-supervised Support Vector Machine combines self-supervised pretraining — learning representations from unlabeled data via pretext tasks — with a Support Vector Machine classifier trained on the resulting features. This hybrid approach enables strong classification performance even when labeled data is scarce, by leveraging the structure embedded in large unlabeled datasets before applying the SVM's margin-maximization objective.	Kernel Principal Component Analysis (Kernel PCA) is a nonlinear dimensionality-reduction method introduced by Bernhard Schölkopf, Alexander Smola, and Klaus-Robert Müller in 1997–1998. It extends classical linear PCA to curved, non-linear data manifolds by implicitly mapping input data into a high-dimensional feature space via a kernel function, then performing standard PCA in that space — all without ever computing the mapping explicitly.