Survey on distance metric learning and dimensionality reduction in data mining
Survey on distance metric learning and dimensionality reduction in data mining
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DOI：
10.1007/s10618-014-0356-z
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发表时间：
2015-03-01
影响因子：
4.8
通讯作者：
Sun, Jimeng
中科院分区：
文献类型：
--
作者：
Wang, Fei;Sun, Jimeng
远程度量学习是数据挖掘和知识发现中的一个基本问题。许多代表性的数据挖掘算法，例如最近邻分类器、层次聚类和谱聚类，严重依赖于底层距离度量来正确测量输入数据之间的关系。近年来，许多研究从理论上或实证上证明，学习良好的距离度量可以极大地提高分类、聚类和检索任务的性能。在本次调查中，我们根据通用框架概述了现有的距离度量学习方法。具体来说，根据距离度量学习过程中可用的监督信息，我们将每种距离度量学习算法分类为监督、无监督或半监督。我们比较了这些不同类型的度量学习方法，指出它们的优点和局限性。最后，我们总结了距离度量学习中的开放挑战，并提出了距离度量学习的未来方向。
Distance metric learning is a fundamental problem in data mining and knowledge discovery. Many representative data mining algorithms, such as -nearest neighbor classifier, hierarchical clustering and spectral clustering, heavily rely on the underlying distance metric for correctly measuring relations among input data. In recent years, many studies have demonstrated, either theoretically or empirically, that learning a good distance metric can greatly improve the performance of classification, clustering and retrieval tasks. In this survey, we overview existing distance metric learning approaches according to a common framework. Specifically, depending on the available supervision information during the distance metric learning process, we categorize each distance metric learning algorithm as supervised, unsupervised or semi-supervised. We compare those different types of metric learning methods, point out their strength and limitations. Finally, we summarize open challenges in distance metric learning and propose future directions for distance metric learning.