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| 준지도 학습 거리 학습× | 메트릭 학습× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2007–2008 | 2003 (foundational); refined 2009 (LMNN) |
| 창시자≠ | Yeung, D.-Y. & Chang, H.; Davis, J. V. & Dhillon, I. S. | Xing, E. P.; Jordan, M. I.; Russell, S.; Ng, A. Y. |
| 유형≠ | Hybrid supervised/unsupervised distance learning | Representation learning / supervised distance optimization |
| 원전≠ | Yeung, D.-Y., & Chang, H. (2007). A kernel approach for semi-supervised metric learning. IEEE Transactions on Neural Networks, 18(1), 141–149. DOI ↗ | 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 ↗ |
| 별칭 | SSML, semi-supervised distance learning, constrained metric learning, weakly supervised metric learning | Distance Metric Learning, Similarity Learning, DML, Representation Learning via Distance |
| 관련 | 5 | 5 |
| 요약≠ | 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. | 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. |
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