图 书 馆
图的半定排序Semidefinite ranking on graphs |
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| 课程网址: | http://videolectures.net/sicgt07_vembu_srog/ |
| 主讲教师: | Shankar Vembu |
| 开课单位: | 伊利诺伊大学 |
| 开课时间: | 2007-09-07 |
| 课程语种: | 英语 |
| 中文简介: |  我们考虑在给定一些偏好关系的情况下对无向图的顶点进行排序的问题。在 [1] 中使用谱松弛之前，已经解决了这种对图问题的排名。他们的方法与光谱聚类算法中的光谱松弛密切相关。在聚类中发现的谱松弛的一个问题是，即使在简单的玩具图上，谱解也可以任意远离最佳解 [2]。最近已经表明，在许多情况下，半定松弛提供了比光谱松弛更好的解决方案，用于聚类 [3] 和转导分类 [4]。因此，我们研究了图上排名的半定松弛。 |
| 课程简介: | We consider the problem of ranking the vertices of an undirected graph given some preference relation. This ranking on graphs problem has been tackled before using spectral relaxations in [1]. Their approach is strongly related to the spectral relaxation made in spectral clustering algorithms. One problem with spectral relaxations that has been found in clustering is that even on simple toy graphs the spectral solution can be arbitrarily far from the optimal one [2]. It has recently been shown that semidefinite relaxations offer in many cases better solutions than spectral ones for clustering [3] and transductive classification [4]. We therefore investigate semidefinite relaxations of ranking on graphs. |
| 关 键 词: | 无向图; 光谱松弛; 半定松弛 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-07-19：nkq |
| 最后编审: | 2021-07-19：nkq |
| 阅读次数: | 22 |