L
1) criterion. Examples of ultrametric and additive trees fitted to two extant data sets are given, plus a Monte Carlo analysis to assess the impact of both typical data error and extreme values on fitted trees. Solutions are compared to the least-squares (L 2) approach of Hubert and Arabie (1995a), with results indicating that (with these data) the L 1 and L 2 optimization strategies perform very similarly. A number of observations are made concerning possible uses of an L 1 approach, the nature and number of identified locally optimal solutions, and metric recovery differences between ultrametrics and additive trees.
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Smith, T. Constructing Ultrametric and Additive Trees Based on the L 1 Norm. J. of Classification 18, 185–207 (2001). https://doi.org/10.1007/s00357-001-0015-0
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DOI: https://doi.org/10.1007/s00357-001-0015-0