The advent of compact, handheld devices has given us a pool of tracked
movement data that could be used to infer trends and patterns that can be made
to use. With this flooding of various trajectory data of animals, humans,
vehicles, etc., the idea of ANALYTiC originated, using active learning to infer
semantic annotations from the trajectories by learning from sets of labeled
data. This study explores the application of dimensionality reduction and
decision boundaries in combination with the already present active learning,
highlighting patterns and clusters in data. We test these features with three
different trajectory datasets with objective of exploiting the the already
labeled data and enhance their interpretability. Our experimental analysis
exemplifies the potential of these combined methodologies in improving the
efficiency and accuracy of trajectory labeling. This study serves as a
stepping-stone towards the broader integration of machine learning and visual
methods in context of movement data analysis.

使用主动学习和降维技术结合决策边界，提取轨迹数据中的模式和聚类特征，以提高轨迹标记的效率和准确性。