基于Baire（ultra）度量的快速红移聚类
Fast redshift clustering with the Baire (ultra) metric
摘要：
与标准的二次时间凝聚层次聚类算法相比，Baire度量在数据集上导出了一个超度量，并且具有线性的计算复杂性。在这项工作中，我们将贝尔距离应用于斯隆数字巡天的光谱和光度红移，使用了大约50万个天文物体。我们想知道光谱红移（更容易确定）可以预测（更容易获得的）光度红移，即我们寻求回归光谱的光度红移，我们为此开发了一个聚类最近邻回归程序。
英文摘要：
The Baire metric induces an ultrametric on a dataset and is of linear computational complexity, contrasted with the standard quadratic time agglomerative hierarchical clustering algorithm. We apply the Baire distance to spectrometric and photometric redshifts from the Sloan Digital Sky Survey using, in this work, about half a million astronomical objects. We want to know how well the (more cos\ tly to determine) spectrometric redshifts can predict the (more easily obtained) photometric redshifts, i.e. we seek to regress the spectrometric on the photometric redshifts, and we develop a clusterwise nearest neighbor regression procedure for this.
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