Indyk, Piotr ;
Kleinberg, Robert ;
Mahabadi, Sepideh ;
Yuan, Yang
Simultaneous Nearest Neighbor Search
Abstract
Motivated by applications in computer vision and databases, we introduce and study the Simultaneous Nearest Neighbor Search (SNN) problem. Given a set of data points, the goal of SNN is to design a data structure that, given a collection of queries, finds a collection of close points that are compatible with each other. Formally, we are given k query points Q=q_1,...,q_k, and a compatibility graph G with vertices in Q, and the goal is to return data points p_1,...,p_k that minimize (i) the weighted sum of the distances from q_i to p_i and (ii) the weighted sum, over all edges (i,j) in the compatibility graph G, of the distances between p_i and p_j. The problem has several applications in computer vision and databases, where one wants to return a set of *consistent* answers to multiple related queries. Furthermore, it generalizes several wellstudied computational problems, including Nearest Neighbor Search, Aggregate Nearest Neighbor Search and the 0extension problem.
In this paper we propose and analyze the following general twostep method for designing efficient data structures for SNN. In the first step, for each query point q_i we find its (approximate) nearest neighbor point p'_i; this can be done efficiently using existing approximate nearest neighbor structures. In the second step, we solve an offline optimization problem over sets q_1,...,q_k and p'_1,...,p'_k; this can be done efficiently given that k is much smaller than n. Even though p'_1,...,p'_k might not constitute the optimal answers to queries q_1,...,q_k, we show that, for the unweighted case, the resulting algorithm satisfies a O(log k/log log k)approximation guarantee. Furthermore, we show that the approximation factor can be in fact reduced to a constant for compatibility graphs frequently occurring in practice, e.g., 2D grids, 3D grids or planar graphs.
Finally, we validate our theoretical results by preliminary experiments. In particular, we show that the empirical approximation factor provided by the above approach is very close to 1.
BibTeX  Entry
@InProceedings{indyk_et_al:LIPIcs:2016:5936,
author = {Piotr Indyk and Robert Kleinberg and Sepideh Mahabadi and Yang Yuan},
title = {{Simultaneous Nearest Neighbor Search}},
booktitle = {32nd International Symposium on Computational Geometry (SoCG 2016)},
pages = {44:144:15},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {9783959770095},
ISSN = {18688969},
year = {2016},
volume = {51},
editor = {S{\'a}ndor Fekete and Anna Lubiw},
publisher = {Schloss DagstuhlLeibnizZentrum fuer Informatik},
address = {Dagstuhl, Germany},
URL = {http://drops.dagstuhl.de/opus/volltexte/2016/5936},
URN = {urn:nbn:de:0030drops59360},
doi = {10.4230/LIPIcs.SoCG.2016.44},
annote = {Keywords: Approximate Nearest Neighbor, Metric Labeling, 0extension, Simultaneous Nearest Neighbor, Group Nearest Neighbor}
}
2016
Keywords: 

Approximate Nearest Neighbor, Metric Labeling, 0extension, Simultaneous Nearest Neighbor, Group Nearest Neighbor 
Seminar: 

32nd International Symposium on Computational Geometry (SoCG 2016)

Issue date: 

2016 
Date of publication: 

2016 