Measuring the Difficulty of Distance-Based Indexing

机译：测量基于距离的索引的难度

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Data structures for similarity search are commonly evaluated on data in vector spaces, but distance-based data structures are also applicable to non-vector spaces with no natural concept of dimensionality. The intrinsic dimensionality statistic of Chavez and Navarro provides a way to compare the performance of similarity indexing and search algorithms across different spaces, and predict the performance of index data structures on non-vector spaces by relating them to equivalent vector spaces. We characterise its asymptotic behaviour, and give experimental results to calibrate these comparisons.

机译：用于相似性搜索的数据结构通常是根据矢量空间中的数据进行评估的，但是基于距离的数据结构也适用于没有维数自然概念的非矢量空间。 Chavez和Navarro的内在维数统计量提供了一种比较相似索引和搜索算法在不同空间上的性能的方法，并通过将非矢量空间上的索引数据结构与等效矢量空间相关联来预测索引数据结构的性能。我们表征其渐近行为，并给出实验结果以校准这些比较。

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来源
《International Conference on String Processing and Information Retrieval(SPIRE 2005); 20051102-04; Buenos Aires(AR)》|2005年|P.103-114|共12页
会议地点 Buenos Aires(AR)
作者
Matthew Skala;
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University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada;

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原文格式 PDF
正文语种 eng
中图分类数据备份与恢复;
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机译：测量基于距离的索引的难度

Measuring the Difficulty of Distance-Based Indexing

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