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arXiv · 1910.00773

Approximating the Geometric Edit Distance

Kyle Fox·Xinyi Li
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Abstract

Edit distance is a measurement of similarity between two sequences such as strings, point sequences, or polygonal curves. Many matching problems from a variety of areas, such as signal analysis, bioinformatics, etc., need to be solved in a geometric space. Therefore, the geometric edit distance (GED) has been studied. In this paper, we describe the first strictly sublinear approximate near-linear time algorithm for computing the GED of two point sequences in constant dimensional Euclidean space. Specifically, we present a randomized (O(n\log^2n)) time (O(\sqrt n))-approximation algorithm. Then, we generalize our result to give a randomized $\alpha$-approximation algorithm for any $\alpha\in [\sqrt{\log n}, \sqrt{n / \log n}]$, running in time $O(n^2/\alpha^2 \log n)$. Both algorithms are Monte Carlo and return approximately optimal solutions with high probability.

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Kyle Fox, Xinyi Li. 2019-10-02. Approximating the Geometric Edit Distance. https://arxiv.org/abs/1910.00773

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