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Subspace Tree

机译:子空间树

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摘要

We are interested in designing a data structure for n objects of dimension d, with the following objectives: Space requirements should be O(d middot n) and the query time should be O(d middot log(n)). Such a structure corresponds to subspace trees. A subspace tree divides the distances between the subspaces. It is realized by the hierarchical linear subspace method. By doing so, the data is divided into disjoint entities. The asymptotic upper bound estimation of the maximum applicable number of subspaces is logarithmically constrained by the number of represented elements and their dimension.The search in such a tree starts at the subspace with the lowest dimension. In this subspace, the set of all possible similar objects is determined. In the next subspace, additional metric information corresponding to a higher dimension is used to reduce this set.
机译:我们感兴趣的是为n个维度为d的对象设计一个数据结构,其目标如下:空间需求应为O(d middot n),查询时间应为O(d middot log(n))。这样的结构对应于子空间树。子空间树划分子空间之间的距离。它是通过分层线性子空间方法实现的。通过这样做,数据被分成不相交的实体。子空间的最大可应用数量的渐近上限估计受表示元素的数量及其维数对数限制。在这种树中的搜索从维数最小的子空间开始。在该子空间中,确定所有可能的相似对象的集合。在下一个子空间中,使用与更高维度相对应的其他度量信息来减少此集合。

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