An analysis of alignment and integral based kernels for machine learning from vessel trajectories

In this paper we present an analysis of the application of the two most important types of similarity measures for moving object trajectories in machine learning from vessel movement data. These similarities are applied in the tasks of clustering, classification and outlier detection. The first similarity type are alignment measures, such as dynamic time warping and edit distance. The second type are based on the integral over time between two trajectories. Following earlier work we define these measures in the context of kernel methods, which provide state-of-the-art, robust algorithms for the tasks studied. Furthermore, we include the influence of applying piecewise linear segmentation as pre-processing to the vessel trajectories when computing alignment measures, since this has been shown to give a positive effect in computation time and performance. experiments the alignment based measures show the best performance. Regular versions of edit distance give the best performance in clustering and classification, whereas the softmax variant of dynamic time warping works best in outlier detection. Moreover, piecewise linear segmentation has a positive effect on alignments, due to the fact that salient points in a trajectory, especially important in clustering and outlier detection, are highlighted by the segmentation and have a large influence in the alignments. Based on our experiments, integral over time based similarity measures are not well-suited for learning from vessel trajectories.