Machine learningMachine learning
半监督度量学习
半监督度量学习通过结合少量标记的成对约束——必须链接对和不能链接对——以及大量未标记数据的几何结构，来学习任务适应性距离函数。其结果是马氏距离或基于核的距离，该距离同时反映了监督信息和数据拓扑，从而改进了最近邻分类和聚类等下游任务。
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来源
- Yeung, D.-Y., & Chang, H. (2007). A kernel approach for semi-supervised metric learning. IEEE Transactions on Neural Networks, 18(1), 141–149. DOI: 10.1109/TNN.2006.883723 ↗
- Davis, J. V., & Dhillon, I. S. (2008). Structured metric learning for high dimensional problems. Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 195–203. DOI: 10.1145/1401890.1401918 ↗
如何引用本页
ScholarGate. (2026, June 3). Semi-supervised Metric Learning. ScholarGate. https://scholargate.app/zh/machine-learning/semi-supervised-metric-learning
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