Data structures and algorithms for nearest neighbor search in general metric spaces
Peter N. Yianilos
- pp 311-321
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
Citations
More filters
Proceedings ArticleDOI
An intelligent Locally Sensitive Hashing based algorithm for data searching
TL;DR: An intelligent searching algorithm called LSH-SmithWaterman is presented that intelligently integrates LSH and Smith-Waterman algorithm to utilize their strengths and exploit their fullest capacities in the field of database searching and querying.
Posted Content
Exact and/or Fast Nearest Neighbors.
TL;DR: Certified Cosine is presented, a novel approach to nearest-neighbors which takes advantage of structure present in the cosine similarity distance metric to offer certificates, which outperform previous exact nearest neighbor methods on these datasets.
Exploring Intersection Trees for Indexing Metric Spaces.
TL;DR: This work introduces a study of a variant of a metric tree data structure for indexing and querying such data, and demonstrates the efficiency of this proposal through experiments on real-world data, as well as a comparison with existing techniques.
Book ChapterDOI
Pruning Algorithms for Low-Dimensional Non-metric k-NN Search: A Case Study.
Leonid Boytsov,Eric Nyberg +1 more
TL;DR: In this article, the authors focus on low-dimensional non-metric search, where tree-based approaches permit efficient and accurate retrieval while having short indexing time, and they consider two known data-driven approaches to extend these rules to nonmetric spaces: TriGen and a piece-wise linear approximation of the pruning rule.
Journal ArticleDOI
Efficient Similarity Search on Quasi-Metric Graphs
TL;DR: A new notion called quasi-metric graph is introduced that connects metric data using a graph, and two simple efficient approaches are proposed, which traverse the quasi-Metric graph following the best-first and the breadth-first paradigms, respectively, and utilize the triangle inequality to prune unnecessary evaluation.
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.