All Near Neighbor GraphWithout Searching
Given a collection of n objects equipped with a distance function d(·, ·), the Nearest Neighbor Graph (NNG) consists in finding the nearest neighbor of each object in the collection. Without an index the total cost of NNG is quadratic. Using an index the cost would be sub-quadratic if the search for...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Postgraduate Office, School of Computer Science, Universidad Nacional de La Plata
2018-04-01
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| Series: | Journal of Computer Science and Technology |
| Subjects: | |
| Online Access: | http://journal.info.unlp.edu.ar/JCST/article/view/695 |
| Summary: | Given a collection of n objects equipped with a distance function d(·, ·), the Nearest Neighbor Graph (NNG) consists in finding the nearest neighbor of each object in the collection. Without an index the total cost of NNG is quadratic. Using an index the cost would be sub-quadratic if the search for individual items is sublinear. Unfortunately, due to the so called curse of dimensionality the indexed and the brute force methods are almost equally inefficient. In this paper we present an efficient algorithm to build the Near Neighbor Graph (nNG), that is an approximation of NNG, using only the index construction, without actually searching for objects. |
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| ISSN: | 1666-6046 1666-6038 |