论文标题
可解释的本地自适应最近的邻居
Interpretable Locally Adaptive Nearest Neighbors
论文作者
论文摘要
当训练自动化系统时，已证明它可以通过学习特定于问题的指标来调整数据的表示是有益的。该指标是全球性的。我们扩展了这个想法，对于广泛使用的K最近邻居算法的家族，我们开发了一种允许学习本地自适应指标的方法。这些本地指标不仅可以提高性能，而且可以自然解释。为了证明我们的方法的工作方式的重要方面，我们对合成数据集进行了许多实验，并显示了其对现实世界基准数据集的有用性。
When training automated systems, it has been shown to be beneficial to adapt the representation of data by learning a problem-specific metric. This metric is global. We extend this idea and, for the widely used family of k nearest neighbors algorithms, develop a method that allows learning locally adaptive metrics. These local metrics not only improve performance but are naturally interpretable. To demonstrate important aspects of how our approach works, we conduct a number of experiments on synthetic data sets, and we show its usefulness on real-world benchmark data sets.
