TLAESA快速神经网络搜索算法中的动态插入

L. Micó, J. Oncina
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引用次数: 2

摘要

最近邻居搜索(NNS)是一种广泛应用于模式识别的技术。为了加快检索速度,人们提出了许多索引技术。由于需要在交互式或在线系统中使用大型动态数据库,因此人们越来越有兴趣调整或创建快速方法来更新这些索引。TLAESA是一种亚线性开销的快速搜索算法,它利用分支定界技术,可以通过极低的距离计算次数找到最近的邻居。本文提出了一种新的TLAESA索引快速更新方法。本文从理论和实验两方面分析了该指标的性能。我们得到了重建期望时间的对数平方上界。该界已在几个合成实验和实际数据实验中得到了验证。
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Dynamic Insertions in TLAESA Fast NN Search Algorithm
Nearest Neighbour search (NNS) is a widely used technique in Pattern Recognition. In order to speed up the search many indexing techniques have been proposed. The need to work with large dynamic databases, in interactive or online systems, has resulted in an increasing interest in adapting or creating fast methods to update these indexes. TLAESA is a fast search algorithm with sub linear overhead that, using of a branch and bound technique, can find the nearest neighbour computing a very low number of distance computations. In this paper, we propose a new fast updating method for the TLAESA index. The behaviour of this index has been analysed theoretical and experimentally. We have obtained a log-square upper bound of the rebuilding expected time. This bound has been verified experimentally on several synthetic and real data experiments.
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