Сравнение методов

	Байесовское обучение метрик	Метрическое обучение
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2010s	2003 (foundational); refined 2009 (LMNN)
Автор метода	Multiple (Xing et al. 2002; Weinberger & Saul 2009; probabilistic extensions by various authors ~2010s)	Xing, E. P.; Jordan, M. I.; Russell, S.; Ng, A. Y.
Тип	Probabilistic distance metric learning	Representation learning / supervised distance optimization
Основополагающий источник	Weinberger, K. Q., & Saul, L. K. (2009). Distance metric learning for large margin nearest neighbor classification. Journal of Machine Learning Research, 10, 207–244.	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.
Другие названия	BML, probabilistic metric learning, Bayesian distance metric learning, Bayesian similarity learning	Distance Metric Learning, Similarity Learning, DML, Representation Learning via Distance
Связанные	5	5
Сводка	Bayesian Metric Learning frames the problem of learning a task-adapted distance function as probabilistic inference. Rather than producing a single optimal metric matrix, it places a prior over metrics, updates it with pairwise similarity or label constraints, and yields a posterior distribution that quantifies uncertainty about which metric best captures the true structure of the data.	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.