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| 강건 거리 학습× | 준지도 학습 거리 학습× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2009–2012 | 2007–2008 |
| 창시자≠ | Various (Weinberger, Saul, Schultz et al.; robust extensions by Shen, Cao and others, 2009–2012) | Yeung, D.-Y. & Chang, H.; Davis, J. V. & Dhillon, I. S. |
| 유형≠ | Supervised/semi-supervised distance metric learning with robustness to noise and outliers | Hybrid supervised/unsupervised distance learning |
| 원전≠ | Shen, C., Kim, J., Wang, L., & van den Hengel, A. (2012). Positive Semidefinite Metric Learning Using Boosting-like Algorithms. Journal of Machine Learning Research, 13, 1007–1036. link ↗ | Yeung, D.-Y., & Chang, H. (2007). A kernel approach for semi-supervised metric learning. IEEE Transactions on Neural Networks, 18(1), 141–149. DOI ↗ |
| 별칭 | robust distance metric learning, noise-robust metric learning, outlier-robust similarity learning, robust DML | SSML, semi-supervised distance learning, constrained metric learning, weakly supervised metric learning |
| 관련 | 5 | 5 |
| 요약≠ | Robust Metric Learning learns a Mahalanobis distance function from labeled or pairwise-constrained data while actively resisting the distortion caused by noisy labels, corrupted examples, or outliers. By replacing standard hinge or squared losses with robust alternatives and adding regularization, it produces a distance metric that generalises well even when the training set is imperfect — a common situation in real-world scientific and applied tasks. | 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. |
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