A (Hilbert) geometric algorithm for approximating the halfspace depth of a point in a convex body
Purvi Gupta, Anant Narayanan

TL;DR
This paper introduces the first deterministic algorithm for approximating the halfspace depth of a point within a convex body, utilizing geometric structures based on the Hilbert metric to improve computational efficiency.
Contribution
The work presents a novel deterministic algorithm for approximating halfspace depth in convex bodies and develops a data structure for approximate membership queries using Hilbert metric-based geometry.
Findings
First deterministic algorithm for depth approximation in convex bodies
Data structure for approximate membership queries based on Hilbert metric
Algorithm for exact depth calculation in planar convex polygons
Abstract
Halfspace (or Tukey) depth is a fundamental and robust measure of centrality of data points in multivariate datasets. Computing the depth of a point with respect to the uniform distribution on an open convex body in is a natural algorithmic problem. While the coarser task of testing membership in convex bodies has been extensively studied, the refined problem of evaluating depth has received comparatively little attention in the literature. In this work, we present an algorithm for approximating the halfspace depth of a point in an open convex body To the best of our knowledge, this is the first deterministic algorithm for this problem. As part of our approach, we design an algorithm for answering approximate membership queries for the depth-trimmed regions of (i.e., the superlevel sets of the depth function). Our data structure is inspired…
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Taxonomy
TopicsAdvanced Graph Theory Research · Computational Geometry and Mesh Generation · Optimization and Search Problems
