2011/04/20 by Fionn Murtagh, Pedro Contreras
Computer Science · Mathematics · Physics and Astronomy · #Artificial intelligence #Astrophysics #Cluster analysis #Computer science #Data Management and Algorithms #Data Visualization and Analytics #Discrete mathematics #Hierarchical clustering #Mathematics #Metric (unit) #Metric space #Physics #Quadratic equation #Redshift #Sky #Ultrametric space #acm:11S82 #acm:62H30 #acm:85-08 #advanced mathematical theories #astro-ph.IM #cs.IR #msc:11S82 #msc:62H30 #msc:85-08 #stat.ML
paper · pdf · doi:10.1142/9789814383295_0005
14 pages, 6 figures
arxiv created 2011/04/20 · openalex publication_date 2011/12/01 · arxiv updated 2017/08/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.