We investigate techniques for similarity analysis of spatio-temporal trajectories for mobile objects. Such data may contain a large number of outliers, which degrade the performance of Euclidean and time warping distance. Therefore, we propose the use of non-metric distance functions based on the longest common subsequence (LCSS), in conjunction with a sigmoidal matching function. Finally, we compare these new methods to various Lp norms and also to time warping distance (for real and synthetic data) and present experimental results that validate the accuracy and efficiency of our approach, especially in the presence of noise.
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机译:我们研究移动对象的时空轨迹的相似性分析技术。这样的数据可能包含大量异常值,从而降低了欧几里得的性能和时间扭曲距离。因此,我们建议使用基于最长公共子序列(LCSS)的非度量距离函数以及S型匹配函数。最后,我们将这些新方法与各种L p sub>规范以及时间扭曲距离(对于真实数据和合成数据)进行了比较,并提供了实验结果,验证了我们方法的准确性和效率,尤其是在存在的情况下的噪音。
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