Semisupervised Metric Learning by Maximizing Constraint Margin
Semisupervised Metric Learning by Maximizing Constraint Margin
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DOI：
10.1109/tsmcb.2010.2101593
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发表时间：
2011-08
期刊：
影响因子：
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通讯作者：
Fei Wang;Shouchun Chen;Changshui Zhang;Ta-Hsin Li
中科院分区：
文献类型：
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作者：
Fei Wang;Shouchun Chen;Changshui Zhang;Ta-Hsin Li
距离度量学习是监督学习领域研究了很长时间的一个老问题。本文研究了在一些弱监督信息的指导下学习合适距离度量的问题。具体来说，这些信息以成对约束的形式存在，它指定一对数据点是在同一个类（必须链接约束）还是在不同的类（不能链接约束）中。在给定这些约束条件下，我们的算法旨在学习一个距离度量，在该度量条件下，具有必须连接约束的点被推得尽可能近，同时具有不可连接约束的点被拉得尽可能远。为了解决非线性问题，我们还推导了算法的核化版本。此外，由于在许多情况下，数据对象（如图像和视频）更自然地表示为高阶张量而不是向量，因此我们还扩展了我们的算法，以便直接从张量中学习度量。最后给出了实验结果，验证了该方法的有效性。
Distance-metric learning is an old problem that has been researched in the supervised-learning field for a very long time. In this paper, we consider the problem of learning a proper distance metric under the guidance of some weak supervisory information. Specifically, this information is in the form of pairwise constraints which specify whether a pair of data points is in the same class ( must-link constraints) or in different classes ( cannot-link constraints). Given those constraints, our algorithm aims to learn a distance metric under which the points with must-link constraints are pushed as close as possible, while simultaneously, the points with cannot-link constraints are pulled away as far as possible. The kernelized version of our algorithm is also derived to tackle the nonlinear problem. Moreover, since in many cases, the data objects, such as images and videos, are more naturally represented as higher order tensors than vectors, we also extend our algorithm to learn the metrics directly from the tensors. Finally, experimental results are presented to show the effectiveness of our method.