图 书 馆
常见的子结构的多个图形高斯模型学习Common Substructure Learning of Multiple Graphical Gaussian Models |
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| 课程网址: | http://videolectures.net/ecmlpkdd2011_hara_common/ |
| 主讲教师: | Satoshi Hara |
| 开课单位: | 大阪大学 |
| 开课时间: | 2011-10-03 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Learning underlying mechanisms of data generation is of great interest in the scientific and engineering fields amongst others. Finding dependency structures among variables in the data is one possible approach for the purpose, and is an important task in data mining. In this paper, we focus on learning dependency substructures shared by multiple datasets. In many scenarios, the nature of data varies due to a change in the surrounding conditions or non-stationary mechanisms over the multiple datasets. However, we can also assume that the change occurs only partially and some relations between variables remain unchanged. Moreover, we can expect that such commonness over the multiple datasets is closely related to the invariance of the underlying mechanism. For example, errors in engineering systems are usually caused by faults in the sub-systems with the other parts remaining healthy. In such situations, though anomalies are observed in sensor values, the underlying invariance of the healthy sub-systems is still captured by some steady dependency structures before and after the onset of the error. We propose a structure learning algorithm to find such invariances in the case of Graphical Gaussian Models (GGM). The proposed method is based on a block coordinate descent optimization, where subproblems can be solved efficiently by existing algorithms for Lasso and the continuous quadratic knapsack problem. We confirm the validity of our approach through numerical simulations and also in applications with real world datasets extracted from the analysis of city-cycle fuel consumption and anomaly detection in car sensors. |
| 关 键 词: | 数据生成机制的学习; 数据集的共性; 结构学习算法 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-24：yumf |
| 阅读次数: | 57 |