方法对比
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| 度量学习× | 半监督学习× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2003 (foundational); refined 2009 (LMNN) | 1970s–2006 (formalized) |
| 提出者≠ | Xing, E. P.; Jordan, M. I.; Russell, S.; Ng, A. Y. | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| 类型≠ | Representation learning / supervised distance optimization | Learning paradigm |
| 开创性文献≠ | 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. link ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| 别名 | Distance Metric Learning, Similarity Learning, DML, Representation Learning via Distance | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| 相关 | 5 | 5 |
| 摘要≠ | 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. | Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained. |
| ScholarGate数据集 ↗ |
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