图 书 馆
图数据的整个正则化路径Entire Regularization Paths for Graph Data |
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| 课程网址: | http://videolectures.net/icml07_tsuda_erpg/ |
| 主讲教师: | Koji Tsuda |
| 开课单位: | 马克斯普朗克研究所 |
| 开课时间: | 2007-10-29 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Graph data such as chemical compounds and XML documents are getting more common in many application domains. A main difficulty of graph data processing lies in the intrinsic high dimensionality of graphs, namely, when a graph is represented as a binary feature vector of indicators of all possible subgraph patterns, the dimensionality gets too large for usual statistical methods. We propose an efficient method to select a small number of salient patterns by regularization path tracking. The generation of useless patterns is minimized by progressive extension of the search space. In experiments, it is shown that our technique is considerably more efficient than a simpler approach based on frequent substructure mining. |
| 关 键 词: | 图形数据; 分段线性路径; 分段线性路径; 结构挖掘 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-07-29：yumf |
| 阅读次数: | 39 |