图 书 馆
大容量流数据的在线细化Online Thinning for High Volume Streaming Data |
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| 课程网址: | http://videolectures.net/kdd2017_hunt_streaming_data/ |
| 主讲教师: | Xin J. Hunt |
| 开课单位: | 统计分析系统研究所有限公司 |
| 开课时间: | 2017-12-01 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In an era of ubiquitous large-scale streaming data, the availability of data far exceeds the capacity of expert human analysts. In many settings, such data is either discarded or stored unprocessed in data centers. This paper proposes a method of online data thinning, in which large-scale streaming datasets are winnowed to preserve unique, anomalous, or salient elements for timely expert analysis. At the heart of this proposed approach is an online anomaly detection method based on dynamic, low-rank Gaussian mixture models. Specifically, the high-dimensional covariance matrices associated with the Gaussian components are associated with low-rank models. According to this model, most observations lie near a union of subspaces. The low-rank modeling mitigates the curse of dimensionality associated with anomaly detection for high-dimensional data, and recent advances in subspace clustering and subspace tracking allow the proposed method to adapt to dynamic environments. The resulting algorithms are scalable, efficient, and are capable of operating in real time. Experiments on wide-area motion imagery illustrate the efficacy of the proposed approach. |
| 关 键 词: | 规模数据; 数据细化; 高斯分量 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-03-20：chenxin01 |
| 最后编审: | 2023-05-17：chenxin01 |
| 阅读次数: | 14 |