图 书 馆
基于亚模松弛的无标度网络学习的凸公式A Convex Formulation for Learning Scale-Free Networks via Submodular Relaxatior |
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| 课程网址: | http://videolectures.net/machine_defazio_scale_free_networks/ |
| 主讲教师: | Aaron Defazio |
| 开课单位: | 澳大利亚国立大学 |
| 开课时间: | 2013-06-14 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | A key problem in statistics and machine learning is the determination of network structure from data. We consider the case where the structure of the graph to be reconstructed is known to be scale-free. We show that in such cases it is natural to formulate structured sparsity inducing priors using submodular functions, and we use their Lovasz extension to obtain a convex relaxation. For tractable classes such as Gaussian graphical models, this leads to a convex optimization problem that can be efficiently solved. We show that our method results in an improvement in the accuracy of reconstructed networks for synthetic data. We also show how our prior encourages scale-free reconstructions on a bioinfomatics dataset. |
| 关 键 词: | 机器学习; 网络结构; 子模函数 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-05-15：cwx |
| 阅读次数: | 57 |