In the last few years trajectory classification has been applied to many real problems, basically considering the dimensions of space and time or attributes inferred from these dimensions. However, with the explosion of social media data and the advances in the semantic enrichment of mobility data, a new type of trajectory data has emerged, and the trajectory spatio-temporal points have now multiple and heterogeneous semantic dimensions. By semantic dimensions we mean any type of information that is neither spatial nor temporal. As a consequence, new classification methods are needed to deal with this new type of data. The main challenge is how to automatically select and combine the data dimensions and to discover the subtrajectories that better discriminate the class. In this paper we propose MasterMovelets, a new parameter-free method for trajectory classification which finds the best trajectory partition and dimension combination for robust high dimensional trajectory classification. Experimental results show that our approach outperforms state-of-the-art methods by reducing the classification error up to \(63\%\), indicating that our proposal is very promising for multidimensional sequence data classification.

中文翻译：

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MasterMovelets：为多方面轨迹分类发现异构的Movelet

在过去几年中，轨迹分类已应用于许多实际问题，基本上是考虑了时空的维度或从这些维度推断出的属性。然而，随着社交媒体数据的爆炸式增长和移动性数据语义的丰富化，出现了一种新型的轨迹数据，轨迹时空点现在具有多种多样的语义维度。语义维度是指既不是空间的也不是时间的任何类型的信息。结果，需要新的分类方法来处理这种新型数据。主要的挑战是如何自动选择和组合数据维，以及发现能更好地区分类别的子轨迹。在本文中，我们提出了MasterMovelets，一种用于轨迹分类的新的无参数方法，该方法可以找到最佳的轨迹划分和维数组合，以实现可靠的高维轨迹分类。实验结果表明，通过将分类误差降低到\（63 \％\），我们的方法优于最新方法，这表明我们的建议对于多维序列数据分类非常有前途。