Abstract
In this paper, we propose a new and powerful way to represent trajectories and measure the distance between them using a distributional kernel. Our method has two unique properties: (i) the identity property which ensures that dissimilar trajectories have no short distances, and (ii) a runtime orders of magnitude faster than that of existing distance measures. An extensive evaluation on several large real-world trajectory datasets confirms that our method is more effective and efficient in trajectory retrieval tasks than traditional and deep learning-based distance measures.
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This project is supported by National Natural Science Foundation of China (Grant No. 62076120).
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Shang, Y., Ting, K.M., Wang, Z., Wang, Y. (2024). Distributional Kernel: An Effective and Efficient Means for Trajectory Retrieval. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14649. Springer, Singapore. https://doi.org/10.1007/978-981-97-2262-4_22
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