Pareto quantiles of unlabeled tree objects
The Annals of Statistics · 2018 · Volume 46 · Issue 4
Abstract
In this paper, we consider a set of unlabeled tree objects with topological and geometric properties. For each data object, two curve representations are developed to characterize its topological and geometric aspects. We further define the notions of topological and geometric medians as well as quantiles based on both representations. In addition, we take a novel approach to define the Pareto medians and quantiles through a multi-objective optimization problem. In particular, we study two different objective functions which measure the topological variation and geometric variation, respectively. Analytical solutions are provided for topological and geometric medians and quantiles, and in general, for Pareto medians and quantiles, the genetic algorithm is implemented. The proposed methods are applied to analyze a data set of pyramidal neurons.
Keywords
62G99, 62P10, Data object, Genetic algorithm, multi-objective optimization, object oriented data, tree-structured data
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