图 书 馆
学习生成原型图的信息理论方法An Information Theoretic Approach to Learning Generative Graph Prototypes |
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| 课程网址: | http://videolectures.net/simbad2011_hancock_generative/ |
| 主讲教师: | Edwin Hancock |
| 开课单位: | 约克大学 |
| 开课时间: | 2011-10-17 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We present a method for constructing a generative model for sets of graphs by adopting a minimum description length approach. The method is posed in terms of learning a generative supergraph model from which the new samples can be obtained by an appropriate sampling mechanism. We commence by constructing a probability distribution for the occurrence of nodes and edges over the supergraph. We encode the complexity of the supergraph using the von-Neumann entropy. A variant of EM algorithm is developed to minimize the description length criterion in which the node correspondences between the sample graphs and the supergraph are treated as missing data.The maximization step involves updating both the node correspondence information and the structure of supergraph using graduated assignment. In the experimental part, we demonstrate the practical utility of our proposed algorithm and show that our generative model gives good graph classification results. Besides, we show how to perform graph clustering with Jensen-Shannon kernel and generate new sample graphs. |
| 关 键 词: | 上位图模型; 采样机制; 熵编码超图 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-09-21：cwx |
| 阅读次数: | 22 |