首页> 外文会议>Scientific and statistical database management >Can Shared-Neighbor Distances Defeat the Curse of Dimensionality?
【24h】

Can Shared-Neighbor Distances Defeat the Curse of Dimensionality?

机译:邻居之间的距离可以克服维数的诅咒吗?

获取原文
获取原文并翻译 | 示例

摘要

The performance of similarity measures for search, indexing, and data mining applications tends to degrade rapidly as the dimensionality of the data increases. The effects of the so-called 'curse of dimensionality' have been studied by researchers for data sets generated according to a single data distribution. In this paper, we study the effects of this phenomenon on different similarity measures for multiply-distributed data. In particular, we assess the performance of shared-neighbor similarity measures, which are secondary similarity measures based on the rankings of data objects induced by some primary distance measure. We find that rank-based similarity measures can result in more stable performance than their associated primary distance measures.
机译:随着数据维数的增加,用于搜索,索引和数据挖掘应用程序的相似性度量的性能往往会迅速下降。研究人员针对根据单个数据分布生成的数据集研究了所谓的“维数诅咒”的影响。在本文中,我们研究了这种现象对多重分布数据的不同相似性度量的影响。特别地,我们评估共享邻居相似性度量的性能,这是基于一些主要距离度量引起的数据对象的排名的次要相似性度量。我们发现,基于等级的相似性度量比其相关的主要距离度量可导致更稳定的性能。

著录项

  • 来源
  • 会议地点 Heidelberg(DE);Heidelberg(DE)
  • 作者单位

    National Institute of Informatics 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan;

    Ludwig-Maximilians-Universitat Munchen Oettingenstr. 67, 80538 Munchen, Germany;

    National Institute of Informatics 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan;

    Ludwig-Maximilians-Universitat Munchen Oettingenstr. 67, 80538 Munchen, Germany;

    Ludwig-Maximilians-Universitat Munchen Oettingenstr. 67, 80538 Munchen, Germany;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 TP311.13;
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号