Machine learningMachine learning
鲁棒度量学习
鲁棒度量学习从标记或成对约束的数据中学习马氏距离函数，同时积极抵抗由噪声标签、损坏的样本或异常值引起的失真。通过用鲁棒的替代损失替换标准的合页损失或平方损失，并添加正则化，即使在训练集不完美的情况下，它也能产生泛化良好的距离度量——这在现实世界的科学和应用任务中很常见。
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来源
- 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 ↗
- Cao, Q., Guo, Z.-C., & Ying, Y. (2012). Generalization Bounds for Metric and Similarity Learning. Machine Learning, 102(1), 115–132. link ↗
如何引用本页
ScholarGate. (2026, June 3). Robust Metric Learning (Outlier-Resistant Distance Metric Learning). ScholarGate. https://scholargate.app/zh/machine-learning/robust-metric-learning
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