Approximate Nearest Neighbor Graph via Index Construction
Given a collection of objects in a metric space, the Nearest Neighbor Graph (NNG) associate each node with its closest neighbor under the given metric. It can be obtained trivially by computing the nearest neighbor of every object. To avoid computing every distance pair an index could be construct...
Guardado en:
| Autores principales: | , , , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Inglés |
| Publicado: |
2016
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| Materias: | |
| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/56767 |
| Aporte de: |
| Sumario: | Given a collection of objects in a metric space, the Nearest Neighbor Graph (NNG) associate each node with its closest neighbor under the given metric.
It can be obtained trivially by computing the nearest neighbor of every object.
To avoid computing every distance pair an index could be constructed. Unfortunately, due to the curse of dimensionality the indexed and the brute force methods are almost equally inefficient. This bring the attention to algorithms computing approximate versions of NNG.
The DiSAT is a proximity searching tree. It is hierarchical. The root computes the distances to all objects, and each child node of the root computes the distance to all its subtree recursively. Top levels will have accurate computation of the nearest neighbor, and as we descend the tree this information would be less accurate. If we perform a few rebuilds of the index, taking deep nodes in each iteration, keeping score of the closest known neighbor, it is possible to compute an Approximate NNG (ANNG). Accordingly, in this work we propose to obtain de ANNG by this approach, without performing any search, and we tested this proposal in both synthetic and real world databases with good results both in costs and response quality. |
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