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Computing Bi-Lipschitz Outlier Embeddings into the Line

Authors Karine Chubarian, Anastasios Sidiropoulos

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Author Details

Karine Chubarian
  • Department of Mathematics, Statistics and Computer Science, University of Illinois at Chicago, IL, USA
Anastasios Sidiropoulos
  • Department of Computer Science, University of Illinois at Chicago, IL, USA

Funding

  • Sidiropoulos, Anastasios: Supported by NSF grants CCF-1815145, CCF-1934915, and by NSF CAREER award 1453472.

Cite As Get BibTex

Karine Chubarian and Anastasios Sidiropoulos. Computing Bi-Lipschitz Outlier Embeddings into the Line. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 176, pp. 36:1-36:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020) https://doi.org/10.4230/LIPIcs.APPROX/RANDOM.2020.36

Abstract

The problem of computing a bi-Lipschitz embedding of a graphical metric into the line with minimum distortion has received a lot of attention. The best-known approximation algorithm computes an embedding with distortion O(c²), where c denotes the optimal distortion [Bădoiu et al. 2005]. We present a bi-criteria approximation algorithm that extends the above results to the setting of outliers.
Specifically, we say that a metric space (X,ρ) admits a (k,c)-embedding if there exists K ⊂ X, with |K| = k, such that (X⧵ K, ρ) admits an embedding into the line with distortion at most c. Given k ≥ 0, and a metric space that admits a (k,c)-embedding, for some c ≥ 1, our algorithm computes a (poly(k, c, log n), poly(c))-embedding in polynomial time. This is the first algorithmic result for outlier bi-Lipschitz embeddings. Prior to our work, comparable outlier embeddings where known only for the case of additive distortion.

Subject Classification

ACM Subject Classification
  • Theory of computation → Design and analysis of algorithms
Keywords
  • metric embeddings
  • outliers
  • distortion
  • approximation algorithms

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