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Discovering Heterogeneous Subsequences for Trajectory Classification

Machine Learning 2019-03-20 v1 Machine Learning

Abstract

In this paper we propose a new parameter-free method for trajectory classification which finds the best trajectory partition and dimension combination for robust trajectory classification. Preliminary experiments show that our approach is very promising.

Keywords

Cite

@article{arxiv.1903.07722,
  title  = {Discovering Heterogeneous Subsequences for Trajectory Classification},
  author = {Carlos Andres Ferrero and Lucas May Petry and Luis Otavio Alvares and Willian Zalewski and Vania Bogorny},
  journal= {arXiv preprint arXiv:1903.07722},
  year   = {2019}
}
R2 v1 2026-06-23T08:12:09.869Z