...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Pattern Analysis and Machine Intelligence >What Makes Objects Similar: A Unified Multi-Metric Learning Approach
【24h】

What Makes Objects Similar: A Unified Multi-Metric Learning Approach

机译：什么使对象相似：统一的多指标学习方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Linkages are essentially determined by similarity measures that may be derived from multiple perspectives. For example, spatial linkages are usually generated based on localities of heterogeneous data. Semantic linkages, however, can come from even more properties, such as different physical meanings behind social relations. Many existing metric learning models focus on spatial linkages but leave the rich semantic factors unconsidered. We propose a Unified Multi-Metric Learning ((UML)-L-2) framework to exploit multiple types of metrics with respect to overdetermined similarities between linkages. In (UML)-L-2, types of combination operators are introduced for distance characterization from multiple perspectives, and thus can introduce flexibilities for representing and utilizing both spatial and semantic linkages. Besides, we propose a uniform solver for (UML)-L-2, and the theoretical analysis reflects the generalization ability of (UML)-L-2 as well. Extensive experiments on diverse applications exhibit the superior classification performance and comprehensibility of (UML)-L-2. Visualization results also validate its ability to physical meanings discovery.
机译：链接本质上是由可以从多个角度得出的相似性度量确定的。例如，通常基于异构数据的位置生成空间链接。但是，语义联系可以来自更多的属性，例如社会关系背后的不同物理含义。许多现有的度量学习模型都将重点放在空间链接上，但是没有考虑丰富的语义因素。我们提出了一个统一的多指标学习（（UML）-L-2）框架，以针对链接之间的过度确定的相似性利用多种类型的指标。在（UML）-L-2中，从多种角度介绍了组合运算符的类型以用于距离表征，因此可以引入灵活性，以表示和利用空间和语义链接。此外，我们提出了（UML）-L-2的统一求解器，理论分析也反映了（UML）-L-2的泛化能力。在各种应用程序上进行的广泛实验显示出（UML）-L-2出色的分类性能和可理解性。可视化结果还验证了其物理意义发现的能力。

著录项

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