scispace - formally typeset
Open AccessProceedings ArticleDOI

Data structures and algorithms for nearest neighbor search in general metric spaces

Reads0
Chats0
TLDR
The up-tree (vantage point tree) is introduced in several forms, together‘ with &&ciated algorithms, as an improved method for these difficult search problems in general metric spaces.
Abstract
We consider the computational problem of finding nearest neighbors in general metric spaces. Of particular interest are spaces that may not be conveniently embedded or approximated in Euclidian space, or where the dimensionality of a Euclidian representation 1s very high. Also relevant are high-dimensional Euclidian settings in which the distribution of data is in some sense of lower dimension and embedded in the space. The up-tree (vantage point tree) is introduced in several forms, together‘ with &&ciated algorithms, as an improved method for these difficult search nroblems. Tree construcI tion executes in O(nlog(n i ) time, and search is under certain circumstances and in the imit, O(log(n)) expected time. The theoretical basis for this approach is developed and the results of several experiments are reported. In Euclidian cases, kd-tree performance is compared.

read more

Content maybe subject to copyright    Report

Citations
More filters
Book ChapterDOI

Pivot Selection Strategies for Permutation-Based Similarity Search

TL;DR: This paper compares five pivots selection strategies on three permutation-based similarity access methods and proposes a novel strategy specifically designed for permutations, which is always outperformed by at least one of the tested strategies.
Proceedings ArticleDOI

Matching spatial relations using DB-tree for image retrieval

Xiaobo Li, +1 more
TL;DR: For applications like "Campus Event" image retrieval, the theoretical analysis and experimental results show that the DB-tree approach out-performs 2D-strings in several aspects.
Book ChapterDOI

Geometric Structure of High-Dimensional Data

TL;DR: This chapter introduces the methods for defining the data similarity (or dissimilarity) and introduces the preliminary spectral graph theory to analyze the data geometry.
Book ChapterDOI

Efficient Autotuning of Hyperparameters in Approximate Nearest Neighbor Search

TL;DR: In this article, an algorithm for automatically tuning the hyperparameters of indexing methods based on randomized space-partitioning trees is proposed. But the tuning algorithm requires a grid search in the parameter space and is often impractically slow due to a time-consuming index-building procedure.
Journal Article

Space-Time Tradeoffs for Proximity Searching in Doubling Spaces

TL;DR: In this article, the authors consider approximate nearest neighbor queries in metric spaces of constant doubling dimension and obtain the following space-time tradeoffs: O(log(n/i) + (1/(varepsilon \gamma)) + O(1/(β + ε)-log(1/β)ε)$ space, where β is the error bound.
References
More filters
Book

Introduction to Statistical Pattern Recognition

TL;DR: This completely revised second edition presents an introduction to statistical pattern recognition, which is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field.
Journal ArticleDOI

Voronoi diagrams—a survey of a fundamental geometric data structure

TL;DR: The Voronoi diagram as discussed by the authors divides the plane according to the nearest-neighbor points in the plane, and then divides the vertices of the plane into vertices, where vertices correspond to vertices in a plane.
Journal ArticleDOI

An Algorithm for Finding Best Matches in Logarithmic Expected Time

TL;DR: An algorithm and data structure are presented for searching a file containing N records, each described by k real valued keys, for the m closest matches or nearest neighbors to a given query record.
Journal ArticleDOI

A Branch and Bound Algorithm for Computing k-Nearest Neighbors

TL;DR: The method of branch and bound is implemented in the present algorithm to facilitate rapid calculation of the k-nearest neighbors, by eliminating the necesssity of calculating many distances.
Related Papers (5)