Comparar métodos

	Aprendizaje Robusto de Métricas	Aprendizaje de métricas semisupervisado
Campo	Aprendizaje automático	Aprendizaje automático
Familia	Machine learning	Machine learning
Año de origen	2009–2012	2007–2008
Autor original	Various (Weinberger, Saul, Schultz et al.; robust extensions by Shen, Cao and others, 2009–2012)	Yeung, D.-Y. & Chang, H.; Davis, J. V. & Dhillon, I. S.
Tipo	Supervised/semi-supervised distance metric learning with robustness to noise and outliers	Hybrid supervised/unsupervised distance learning
Fuente seminal	Shen, C., Kim, J., Wang, L., & van den Hengel, A. (2012). Positive Semidefinite Metric Learning Using Boosting-like Algorithms. Journal of Machine Learning Research, 13, 1007–1036.	Yeung, D.-Y., & Chang, H. (2007). A kernel approach for semi-supervised metric learning. IEEE Transactions on Neural Networks, 18(1), 141–149.
Alias	robust distance metric learning, noise-robust metric learning, outlier-robust similarity learning, robust DML	SSML, semi-supervised distance learning, constrained metric learning, weakly supervised metric learning
Relacionados	5	5
Resumen	Robust Metric Learning learns a Mahalanobis distance function from labeled or pairwise-constrained data while actively resisting the distortion caused by noisy labels, corrupted examples, or outliers. By replacing standard hinge or squared losses with robust alternatives and adding regularization, it produces a distance metric that generalises well even when the training set is imperfect — a common situation in real-world scientific and applied tasks.	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.