3 research outputs found
Approximate Nearest Neighbor Search Amid Higher-Dimensional Flats
We consider the Approximate Nearest Neighbor (ANN) problem where the input set consists of n k-flats in the Euclidean Rd, for any fixed parameters k 0 is another prespecified parameter. We present an algorithm that achieves this task with n^{k+1}(log(n)/epsilon)^O(1) storage and preprocessing (where the constant of proportionality in the big-O notation depends on d), and can answer a query in O(polylog(n)) time (where the power of the logarithm depends on d and k). In particular, we need only near-quadratic storage to answer ANN queries amidst a set of n lines in any fixed-dimensional Euclidean space. As a by-product, our approach also yields an algorithm, with similar performance bounds, for answering exact nearest neighbor queries amidst k-flats with respect to any polyhedral distance function. Our results are more general, in that they also
provide a tradeoff between storage and query time
Approximate Nearest-Neighbor Search for Line Segments
Approximate nearest-neighbor search is a fundamental algorithmic problem that
continues to inspire study due its essential role in numerous contexts. In
contrast to most prior work, which has focused on point sets, we consider
nearest-neighbor queries against a set of line segments in , for
constant dimension . Given a set of disjoint line segments in
and an error parameter , the objective is to
build a data structure such that for any query point , it is possible to
return a line segment whose Euclidean distance from is at most
times the distance from to its nearest line segment. We
present a data structure for this problem with storage and query time , where is the spread of the set of
segments . Our approach is based on a covering of space by anisotropic
elements, which align themselves according to the orientations of nearby
segments.Comment: 20 pages (including appendix), 5 figure
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum