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Learning Lines with Ordinal Constraints

Authors Bohan Fan, Diego Ihara , Neshat Mohammadi, Francesco Sgherzi, Anastasios Sidiropoulos, Mina Valizadeh

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Author Details

Bohan Fan
  • Department of Computer Science, University of Illinois at Chicago, IL, USA
Diego Ihara
  • Department of Computer Science, University of Illinois at Chicago, IL, USA
Neshat Mohammadi
  • Department of Computer Science, University of Illinois at Chicago, IL, USA
Francesco Sgherzi
  • Department of Computer Science, University of Illinois at Chicago, IL, USA
Anastasios Sidiropoulos
  • Department of Computer Science, University of Illinois at Chicago, IL, USA
Mina Valizadeh
  • Department of Computer Science, University of Illinois at Chicago, IL, USA

Funding

Supported by NSF grants CCF-1815145, CCF-1934915, and by NSF CAREER award 1453472.

Cite AsGet BibTex

Bohan Fan, Diego Ihara, Neshat Mohammadi, Francesco Sgherzi, Anastasios Sidiropoulos, and Mina Valizadeh. Learning Lines with Ordinal Constraints. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 176, pp. 45:1-45:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2020.45

Abstract

We study the problem of finding a mapping f from a set of points into the real line, under ordinal triple constraints. An ordinal constraint for a triple of points (u,v,w) asserts that |f(u)-f(v)| < |f(u)-f(w)|. We present an approximation algorithm for the dense case of this problem. Given an instance that admits a solution that satisfies (1-ε)-fraction of all constraints, our algorithm computes a solution that satisfies (1-O(ε^{1/8}))-fraction of all constraints, in time O(n⁷) + (1/ε)^{O(1/ε^{1/8})} n.

Subject Classification

ACM Subject Classification
  • Theory of computation → Approximation algorithms analysis
Keywords
  • metric learning
  • embedding into the line
  • ordinal constraints
  • approximation algorithms

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References

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