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: | 在许多应用领域中, 化合物和 xml 文档等图形数据越来越普遍。图形数据处理的一个主要困难在于图形的内在高维数, 即当图形表示为所有可能的子图模式的指标的二进制特征向量时, 维数就会变得太大, 无法用于通常的统计方法。提出了一种通过正则化路径跟踪来选择少量突出模式的有效方法。通过逐步扩展搜索空间, 最大限度地减少了无用模式的生成。实验表明, 我们的技术比一种简单的基于频繁子结构挖掘的方法要有效得多。 |
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: | 43 |