Similarity Search and Applications - 11th International Conference, SISAP 2018, Lima, Peru, October 7-9, 2018, Proceedings
Similarity Search and Applications - 11th International Conference, SISAP 2018, Lima, Peru, October 7-9, 2018, Proceedings
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相似性搜索和应用 - 第 11 届国际会议，SISAP 2018，秘鲁利马，2018 年 10 月 7-9 日，会议记录
DOI：
10.1007/978-3-030-02224-2_3
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Connor R
中科院分区：
文献类型：
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作者：
Connor R
度量搜索技术可以通过查询期间执行距离计算的时间来有效地表征。最精确的搜索机制使用“即时”方法，其中计算距离作为导航策略的一部分。另一种方法是使用“一次性”方法，在每次查询开始时计算到一组固定参考对象的距离。这些距离通常用于将数据和查询重新投射到查询更有效的不同空间，从而获得近似解决方案。在本文中，我们使用“一次性”方法来实现精确搜索机制。一组固定的参考对象用于定义原始空间内的大量区域，并且根据这些区域的定义评估每个查询。当且仅当数据对于查询解决方案的计算有用时，才会访问数据。随着维度的增加，定义区域的数量必须增加，但排除计算所需的内存不会增加。我们表明，该技术在 SISAP 基准数据集上具有出色的性能，最有趣的是，我们展示了如何通过相对适度增加所使用的参考对象数量来抵消维度的增加。
Metric search techniques can be usefully characterised by the time at which distance calculations are performed during a query. Most exact search mechanisms use a “just-in-time” approach where distances are calculated as part of a navigational strategy. An alternative is to use a“one-time” approach, where distances to a fixed set of reference objects are calculated at the start of each query. These distances are typically used to re-cast data and queries into a different space where querying is more efficient, allowing an approximate solution to be obtained.In this paper we use a “one-time” approach for an exact search mechanism. A fixed set of reference objects is used to define a large set of regions within the original space, and each query is assessed with respect to the definition of these regions. Data is then accessed if, and only if, it is useful for the calculation of the query solution.As dimensionality increases, the number of defined regions must increase, but the memory required for the exclusion calculation does not. We show that the technique gives excellent performance over the SISAP benchmark data sets, and most interestingly we show how increases in dimensionality may be countered by relatively modest increases in the number of reference objects used.