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A Systematic Approach for Selecting Trajectories for Data Augmentation

paperactivea-systematic-approach-for-selecting-trajectories-for-data-augmentation-2da3ea7f·1 events·first seen Jun 10, 2026

Aliases: A Systematic Approach for Selecting Trajectories for Data Augmentation

Co-occurring entities

Optuna UMAP

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3arXiv · cs.LG·Jun 10, 2026·source ↗

Systematic framework for selecting trajectories in data augmentation evaluated across five strategies

A thesis-derived arXiv preprint proposes a framework for evaluating five trajectory selection strategies—Outlierness, Diversity, Representativeness, Uncertainty, and Random—for data augmentation in spatio-temporal ML tasks. The study tests these strategies across four datasets spanning animal behavior, maritime, and urban traffic domains using linear and non-linear models with Optuna-based hyperparameter optimization. Key findings show systematic strategies (especially Outlierness and Uncertainty) outperform random selection in sparse datasets but can degrade performance in dense, high-quality datasets, with UMAP visualization confirming topological effects.

Evaluation and Benchmarking Optuna A Systematic Approach for Selecting Trajectories for Data Augmentation UMAP