So sánh phương pháp

	Học khoảng cách mạnh mẽ	Học độ đo bán giám sát
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2009–2012	2007–2008
Người khởi xướng	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.
Loại	Supervised/semi-supervised distance metric learning with robustness to noise and outliers	Hybrid supervised/unsupervised distance learning
Công trình gốc	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.	Yeung, D.-Y., & Chang, H. (2007). A kernel approach for semi-supervised metric learning. IEEE Transactions on Neural Networks, 18(1), 141–149.
Tên gọi khác	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
Liên quan	5	5
Tóm tắt	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.