arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2008.09607cs.DS
收藏

最优度量搜索等价于最小支配集问题

Optimal Metric Search Is Equivalent to the Minimum Dominating Set Problem

Magnus Lie Hetland

摘要:

在度量搜索中,最坏情况分析几乎没有价值,因为搜索总是退化为对行为不良数据的线性扫描。因此,人们花费了大量的精力来更细致地描述实际上可能达到的性能,包括像AESA家族这样的启发式基线,以及像内在维度这样的统计代理。本文通过对任何给定数据集和查询实际可实现的最佳性能的精确描述来解决问题的核心。具体来说,线性时间目标保持减少之间的最佳度量搜索和最小支配集问题,其贪婪的近似成为一个基于甲骨文的AESA的等价物,重复选择的枢轴,消除了大部分剩余的点之间的两个方向。作为一个例子,AESA启发式算法适用于淡化先前消除的点的作用,产生一些适度的性能改进,超过原来的,以及它的年轻的相对iAESA2。

英文摘要:

In metric search, worst-case analysis is of little value, as the search invariably degenerates to a linear scan for ill-behaved data. Consequently, much effort has been expended on more nuanced descriptions of what performance might in fact be attainable, including heuristic baselines like the AESA family, as well as statistical proxies such as intrinsic dimensionality. This paper gets to the heart of the matter with an exact characterization of the best performance actually achievable for any given data set and query. Specifically, linear-time objective-preserving reductions are established in both directions between optimal metric search and the minimum dominating set problem, whose greedy approximation becomes the equivalent of an oracle-based AESA, repeatedly selecting the pivot that eliminates the most of the remaining points. As an illustration, the AESA heuristic is adapted to downplay the role of previously eliminated points, yielding some modest performance improvements over the original, as well as its younger relative iAESA2.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑