Data structures and algorithms for nearest neighbor search in general metric spaces
Peter N. Yianilos
- pp 311-321
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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
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Approximate Best Bin First k-d Tree All Nearest Neighbor Search with Incremental Updates
Jan Kybic,Ivan Vnu +1 more
TL;DR: An approximate algorithm to find all nearest neighbors (NN) for a set of points in moderate to high-dimensional spaces is described and it is observed that the new method is superior for exact search and high number of points as well as for approximate search in small to moderate dimensions or when a fast approximation is needed.
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Treelogy: A benchmark suite for tree traversals
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Searching High-Dimensional Neighbours: CPU-Based Tailored Data-Structures Versus GPU-Based Brute-Force Method
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TL;DR: It is shown that the proposed implementation of the brute-force algorithm using GPU (Graphics Processing Units) programming is up to 150 times faster than the classical approaches on synthetic data, and up to 75 times faster on real image processing algorithms (finding similar patches in images and texture synthesis).
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