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关于图上距离的注意事项

A note of caution regarding distances on graphs
课程网址: http://videolectures.net/icml2010_von_luxburg_ancr/  
主讲教师: Ulrike von Luxburg
开课单位: 马克斯普朗克研究所
开课时间: 2010-07-20
课程语种: 英语
中文简介:
非几何数据通常以图形的形式表示,其中边表示实例之间的相似性或局部关系。利用图的全局结构的一种优雅方式是通过通勤距离(也称为电阻距离)来实现的。据推测,它具有以下特性:来自同一簇的顶点彼此“接近”,而来自不同簇的顶点彼此“远”。随着基础图的大小增加,我们研究通勤距离的行为。我们证明了通勤距离收敛于一个表达式,该表达式没有考虑图形的结构,并且作为图形上的距离函数完全没有意义。因此,对于大型图形和高维度,强烈建议不要将原始通勤距离用于机器学习目的。我们怀疑图表上的其他几个距离也存在类似的行为。
课程简介: Non-geometric data is often represented in form of a graph where edges represent similarity or local relationships between instances. One elegant way to exploit the global structure of the graph is implemented by the commute distance (also known as resistance distance). Supposedly it has the property that vertices from the same cluster are "close" to each other whereas vertices from different clusters are "far" from each other. We study the behavior of the commute distance as the size of the underlying graph increases. We prove that the commute distance converges to an expression that does not take into account the structure of the graph and that is completely meaningless as a distance function on the graph. Consequently, the use of the raw commute distance for machine learning purposes is strongly discouraged for large graphs and in high dimensions. We suspect that a similar behavior holds for several other distances on graphs.
关 键 词: 非几何数据; 图形; 局部关系
课程来源: 视频讲座网
最后编审: 2019-04-25:cwx
阅读次数: 26