Sammenlign metoder

	Online Metric Learning	Metrisk Læring	Online læring
Fagområde	Maskinlæring	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning	Machine learning
Oprindelsesår	2004–2009	2003 (foundational); refined 2009 (LMNN)	1958–2000s
Ophavsperson	Shalev-Shwartz, S.; Singer, Y.; and others	Xing, E. P.; Jordan, M. I.; Russell, S.; Ng, A. Y.	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
Type	Online / incremental learning of distance metrics	Representation learning / supervised distance optimization	Learning paradigm (sequential model update)
Oprindelig kilde	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.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
Aliasser	OML, incremental metric learning, streaming metric learning, online distance metric learning	Distance Metric Learning, Similarity Learning, DML, Representation Learning via Distance	incremental learning, sequential learning, streaming learning, online machine learning
Relaterede	3	5	6
Resumé	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.	Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight.