度量学习的学习邻域
Learning Neighborhoods for Metric Learning
摘要：
度量学习方法已被证明在不同的学习任务上表现良好。它们中的许多依赖于在原始特征空间中计算的目标邻域关系，并且在整个学习过程中保持固定。因此，学习的度量反映了原始的邻域关系。我们提出了一种新的制定的度量学习问题，其中，除了度量，目标邻域关系也学习在一个两步迭代的方法。新的公式可以看作是许多现有的度量学习方法的推广。该公式包括一个目标邻居分配规则，根据其质量分配不同数量的邻居实例;“高质量”的实例得到更多的邻居。我们用它的两个实例进行了实验，它们对应于度量学习算法LMNN和MCML，并将其与许多数据集上的其他度量学习方法进行了比较。实验结果显示了最先进的性能，并提供证据表明学习邻域关系确实提高了预测性能。
英文摘要：
Metric learning methods have been shown to perform well on different learning tasks. Many of them rely on target neighborhood relationships that are computed in the original feature space and remain fixed throughout learning. As a result, the learned metric reflects the original neighborhood relations. We propose a novel formulation of the metric learning problem in which, in addition to the metric, the target neighborhood relations are also learned in a two-step iterative approach. The new formulation can be seen as a generalization of many existing metric learning methods. The formulation includes a target neighbor assignment rule that assigns different numbers of neighbors to instances according to their quality; `high quality' instances get more neighbors. We experiment with two of its instantiations that correspond to the metric learning algorithms LMNN and MCML and compare it to other metric learning methods on a number of datasets. The experimental results show state-of-the-art performance and provide evidence that learning the neighborhood relations does improve predictive performance.
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