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相关概念视频

Ogive Graph01:07

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An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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生成式图形词典学习

Zhichen Zeng1, Ruike Zhu1, Yinglong Xia2

  • 1Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.

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概括
此摘要是机器生成的。

这项研究介绍了FraMe,一种新的图形词典学习 (GDL) 的生成方法. FraMe有效地为复杂的图形数据创建非线性嵌入, 性能优于现有的方法.

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科学领域:

  • 机器学习
  • 图形表示学习
  • 数据挖掘

背景情况:

  • 词典学习对于数据近似至关重要.
  • 图形字典学习 (GDL) 由于不同度量空间而具有挑战性.
  • 现有的GDL方法通常使用昂贵的重建,线性方法.

研究的目的:

  • 为图形词典学习提出一个生成模型.
  • 解决现有的重建GDL方法的局限性.
  • 开发一种能够学习非线性图形嵌入的方法.

主要方法:

  • 引入了融合格罗莫夫-瓦斯斯坦混合模型 (FraMe).
  • 用于图表生成的辐射基函数内核.
  • 使用非线性嵌入空间的FGW距离.
  • 开发了一个快速的预期-最大化算法.

主要成果:

  • FraMe生成的非线性嵌入空间与原来的图形空间近似.
  • 拟议的算法在学习节点和图形嵌入方面表现出有效性.
  • 与最先进的GDL方法相比,取得了显著的改进.

结论:

  • 提供一个有效的生成解决方案来学习图形字典.
  • 该方法为图形数据提供准确的非线性嵌入.
  • 对于图形结构的表示学习领域,