论文标题
与Schatten P-Norms有监督的分类度量学习
Supervised Categorical Metric Learning with Schatten p-Norms
论文作者
论文摘要
公制学习已经成功地学习了适合数值数据集的新指标。但是，其对分类数据的发展仍然需要进一步探索。在本文中，我们提出了一种称为\ emph {分类投影度量学习}的方法，该方法试图有效地〜（即更少的计算时间和更好的预测准确性）解决了分类数据中度量学习的问题。我们利用值距离指标来表示我们的数据，并根据此表示形式提出新距离。然后，我们展示如何有效学习新指标。我们还通过schatten $ p $ norm概括了几个以前的正规化器，并为其提供了概括，以补充标准的度量学习限制。实验结果表明我们的方法提供了
Metric learning has been successful in learning new metrics adapted to numerical datasets. However, its development on categorical data still needs further exploration. In this paper, we propose a method, called CPML for \emph{categorical projected metric learning}, that tries to efficiently~(i.e. less computational time and better prediction accuracy) address the problem of metric learning in categorical data. We make use of the Value Distance Metric to represent our data and propose new distances based on this representation. We then show how to efficiently learn new metrics. We also generalize several previous regularizers through the Schatten $p$-norm and provides a generalization bound for it that complements the standard generalization bound for metric learning. Experimental results show that our method provides
