Сравнение методов

	Онлайновое обучение метрикам	Метрическое обучение
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2004–2009	2003 (foundational); refined 2009 (LMNN)
Автор метода	Shalev-Shwartz, S.; Singer, Y.; and others	Xing, E. P.; Jordan, M. I.; Russell, S.; Ng, A. Y.
Тип	Online / incremental learning of distance metrics	Representation learning / supervised distance optimization
Основополагающий источник	Shalev-Shwartz, S., Singer, Y., & Ng, A. Y. (2004). Online and batch learning of pseudo-metrics. Proceedings of the 21st International Conference on Machine Learning (ICML 2004), pp. 94. ACM.	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	OML, incremental metric learning, streaming metric learning, online distance metric learning	Distance Metric Learning, Similarity Learning, DML, Representation Learning via Distance
Связанные	3	5
Сводка	Online Metric Learning adapts a Mahalanobis distance metric incrementally as new labeled examples or pairwise constraints arrive one at a time, without storing the full dataset. It merges the efficiency of online learning with the representational power of metric learning, making it suitable for streaming, large-scale, or continually changing environments where retraining from scratch is impractical.	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.