图 书 馆
空间损耗：电阻距离的大样本分析Getting lost in space: Large sample analysis of the resistance distance |
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| 课程网址: | http://videolectures.net/nips2010_hein_gls/ |
| 主讲教师: | Matthias Hein |
| 开课单位: | 马克斯普朗克研究所 |
| 开课时间: | 信息不详。欢迎您在右侧留言补充。 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | The commute distance between two vertices in a graph is the expected time it takes a random walk to travel from the first to the second vertex and back. 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 at all 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. As an alternative we introduce the amplified commute distance that corrects for the undesired large sample effects. |
| 关 键 词: | 计算机科学; 网络分析; 机器学习 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-11-22：cwx |
| 阅读次数: | 21 |