期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 39, 期 18, 页码 13426-13439出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2012.05.060
关键词
Vessel trajectories; Moving object trajectories; Piecewise linear segmentation; Alignment measures; Geographical domain knowledge
类别
资金
- Dutch Ministry of Economic Affairs under the BSIK program
In this paper we present a machine learning framework to analyze moving object trajectories from maritime vessels. Within this framework we perform the tasks of clustering, classification and outlier detection with vessel trajectory data. First, we apply a piecewise linear segmentation method to the trajectories to compress them. We adapt an existing technique to better retain stop and move information and show the better performance of our method with experimental results. Second, we use a similarity based approach to perform the clustering, classification and outlier detection tasks using kernel methods. We present experiments that investigate different alignment kernels and the effect of piecewise linear segmentation in the three different tasks. The experimental results show that compression does not negatively impact task performance and greatly reduces computation time for the alignment kernels. Finally, the alignment kernels allow for easy integration of geographical domain knowledge. In experiments we show that this added domain knowledge enhances performance in the clustering and classification tasks. (C) 2012 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据