Comparer des méthodes

	Apprentissage métrique semi-supervisé	Apprentissage métrique
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2007–2008	2003 (foundational); refined 2009 (LMNN)
Auteur d'origine	Yeung, D.-Y. & Chang, H.; Davis, J. V. & Dhillon, I. S.	Xing, E. P.; Jordan, M. I.; Russell, S.; Ng, A. Y.
Type	Hybrid supervised/unsupervised distance learning	Representation learning / supervised distance optimization
Source fondatrice	Yeung, D.-Y., & Chang, H. (2007). A kernel approach for semi-supervised metric learning. IEEE Transactions on Neural Networks, 18(1), 141–149.	Xing, E. P., Jordan, M. I., Russell, S., & Ng, A. Y. (2003). Distance metric learning with application to clustering with side-information. In Advances in Neural Information Processing Systems (NIPS), 16, 505–512.
Alias	SSML, semi-supervised distance learning, constrained metric learning, weakly supervised metric learning	Distance Metric Learning, Similarity Learning, DML, Representation Learning via Distance
Apparentées	5	5
Résumé	Semi-supervised metric learning learns a task-adapted distance function by combining a small set of labeled pairwise constraints — must-link and cannot-link pairs — with the geometric structure of a much larger pool of unlabeled data. The result is a Mahalanobis-style or kernel-based distance that reflects both supervision and data topology, improving downstream tasks such as nearest-neighbor classification and clustering.	Metric learning is a machine-learning framework that trains a distance or similarity function from data so that semantically similar examples end up close together in the learned space while dissimilar examples are pushed apart. Unlike fixed distances such as Euclidean, the learned metric adapts to the structure of the task, making downstream classifiers, clusterers, and retrieval systems significantly more accurate.