文章基本信息
- 标题：Approximate Nearest Neighbor for Curves - Simple, Efficient, and Deterministic
- 本地全文：下载
- 作者：Arnold Filtser ; Omrit Filtser ; Matthew J. Katz 等
- 期刊名称：LIPIcs : Leibniz International Proceedings in Informatics
- 电子版ISSN：1868-8969
- 出版年度：2020
- 卷号：168
- 页码：48:1-48:19
- DOI：10.4230/LIPIcs.ICALP.2020.48
- 出版社：Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik
- 摘要：In the (1+ε,r)-approximate near-neighbor problem for curves (ANNC) under some similarity measure δ, the goal is to construct a data structure for a given set ð'Z of curves that supports approximate near-neighbor queries: Given a query curve Q, if there exists a curve C â^^ ð'Z such that δ(Q,C)⤠r, then return a curve C' â^^ ð'Z with δ(Q,C') ⤠(1+ε)r. There exists an efficient reduction from the (1+ε)-approximate nearest-neighbor problem to ANNC, where in the former problem the answer to a query is a curve C â^^ ð'Z with δ(Q,C) ⤠(1+ε)â<.δ(Q,C^*), where C^* is the curve of ð'Z most similar to Q. Given a set ð'Z of n curves, each consisting of m points in d dimensions, we construct a data structure for ANNC that uses nâ<. O(1/ε)^{md} storage space and has O(md) query time (for a query curve of length m), where the similarity measure between two curves is their discrete Fréchet or dynamic time warping distance. Our method is simple to implement, deterministic, and results in an exponential improvement in both query time and storage space compared to all previous bounds. Further, we also consider the asymmetric version of ANNC, where the length of the query curves is k ⪠m, and obtain essentially the same storage and query bounds as above, except that m is replaced by k. Finally, we apply our method to a version of approximate range counting for curves and achieve similar bounds.
- 关键词：polygonal curves; Fréchet distance; dynamic time warping; approximation algorithms; (asymmetric) approximate nearest neighbor; range counting
