Approximating the Geometric Edit Distance
Kyle J. Fox, Xinyi Li · arXiv (Cornell University) · 2019
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 $α$-approximation algorithm for any $α\in [\sqrt{\log n}, \sqrt{n / \log n}]$, running in time $O(n^2/α^2 \log n)$. Both algorithms are Monte Carlo and return approximately optimal solutions with high probability.