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System and method for performing k-nearest neighbor search based on minimax distance measure and efficient outlier detection

机译:基于最小最大距离测度和有效离群值检测执行k最近邻搜索的系统和方法

摘要

A system and method enable a set of dataset objects that are K-nearest neighbors (K-NN), based on their Minimax distances to a test object, to be identified without computing the all-pair Minimax distances directly. A pairwise distance between the test object and each dataset object is computed. Iteratively, one of the dataset objects is selected to add to a K-NN set until the K-NN set includes a predefined number of nearest neighbors. The selected dataset object at each iteration is the one for which there is no other unselected dataset object which has a smaller pairwise distance to any of a current subset of objects than the selected dataset object. The current subset of objects includes the test object and the dataset objects currently in the K-NN set. After the K-NN set is identified it may be output or used to generate other information, such as a test object label.
机译:一种系统和方法使得能够基于其到测试对象的最小最大距离来识别属于K最近邻居(K-NN)的一组数据集对象,而无需直接计算所有对的最小最大距离。计算测试对象和每个数据集对象之间的成对距离。迭代地,选择数据集对象之一以添加到K-NN集,直到K-NN集包括预定义数量的最近邻居。每次迭代中选定的数据集对象是没有其他未选定的数据集对象的对象,该对象与对象的任何当前子集的成对距离都小于选定的数据集对象。对象的当前子集包括当前在K-NN集中的测试对象和数据集对象。在识别出K-NN集之后,可以将其输出或用于生成其他信息,例如测试对象标签。

著录项

  • 公开/公告号US10073887B2

    专利类型

  • 公开/公告日2018-09-11

    原文格式PDF

  • 申请/专利权人 CONDUENT BUSINESS SERVICES LLC;

    申请/专利号US201514791869

  • 发明设计人 MORTEZA CHEHREGHANI;

    申请日2015-07-06

  • 分类号G06F17/30;G06F7/00;

  • 国家 US

  • 入库时间 2022-08-21 13:06:16

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