A Systematic Approach for Selecting Trajectories for Data Augmentation

• URL: http://arxiv.org/abs/2606.10938v1
• Date: Tue, 09 Jun 2026 14:46:47 GMT
• Title: A Systematic Approach for Selecting Trajectories for Data Augmentation
• Abstract summary: This thesis develops a framework to evaluate five systematic selection strategies: Outlierness, Diversity, Representativeness, Uncertainty, and Random selection.<n>These strategies were rigorously tested across four datasets covering animal behavior, maritime traffic, and urban traffic.<n>The results indicate that, while systematic selection is not a universal solution, it offers advantages over the random baseline.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Trajectory data augmentation is a promising approach to mitigate data scarcity in machine learning applications, but its utility has been limited by the complexity of preserving spatio-temporal coherence. Although prior work demonstrated the viability of geometric perturbation, it relied on naive random selection, leaving a critical gap in understanding which trajectories should be augmented for maximal benefit. This thesis addresses this gap by developing a systematic and scalable framework to evaluate five systematic selection strategies: Outlierness, Diversity, Representativeness, Uncertainty, and Random selection. These strategies were rigorously tested across four datasets covering animal behavior (Foxes and Starkey), maritime traffic (AIS), and urban traffic (Car) using a suite of linear and non-linear machine learning models. As part of this evaluation, an Optuna-based hyperparameter optimization loop was integrated to empirically identify the best-performing augmentation parameters for each dataset within the explored search space. The results indicate that, while systematic selection is not a universal solution, it offers distinct advantages over the random baseline. Systematic strategies, particularly Outlierness and Uncertainty, demonstrated higher stability and were less prone to performance degradation observed with random sampling in dense datasets. However, the findings also reveal that the value of augmentation is strictly conditional. Visual analysis via UMAP demonstrates that while systematic augmentation successfully repairs topological fragmentation in sparse datasets, it can act as a corrupting noise signal in high-quality, dense datasets. Furthermore, the study identified physical limitations in high-velocity domains, where standard perturbation techniques lead to divergence in feature space...

Related papers

• Preference Data Selection for Mitigating the Alignment Tax in Large Language Models [52.18310687523229]
  BALIGN is a balanced data selection strategy that mitigates catastrophic forgetting while optimizing alignment efficacy.<n>By aggregating three key data-centric features into a unified composite risk score, BALIGN systematically filters out high-risk preference samples.<n>Experiments on standard human preference datasets demonstrate that BALIGN strongly preserves foundational capabilities without compromising alignment gains.
  arXiv  Detail & Related papers  (2026-08-25T07:58:42Z)
• How Low Can You Go? Active Learning for Sparse Model Discovery in the Ultra-Low-Data Limit [2.9937025170705582]
  This work introduces an active learning strategy for dynamics discovery in the ultra-low data limit.<n>Rather than sampling randomly, our method prioritizes regions that are most informative for model identification.<n>Across all scenarios, the proposed method accurately identifies the governing dynamics with significantly fewer data samples than random sampling.
  arXiv  Detail & Related papers  (2026-06-10T15:06:59Z)
• VMF-GOS: Geometry-guided virtual Outlier Synthesis for Long-Tailed OOD Detection [10.895746797423223]
  We introduce a Geometry-guided virtual Outlier Synthesis (GOS) strategy that models statistical properties using the von Mises-Fisher (vMF) distribution on a hypersphere.<n>Specifically, we locate a low-likelihood annulus in the feature space and perform directional sampling of virtual outliers in this region.<n>Experiments on benchmarks such as CIFAR-LT demonstrate that our method outperforms sota approaches that utilize external real images.
  arXiv  Detail & Related papers  (2026-02-05T07:58:12Z)
• Unsupervised Feature Selection via Robust Autoencoder and Adaptive Graph Learning [4.371255245299254]
  Unsupervised feature selection (UFS) aims to simultaneously cluster data and identify the most discriminative features.<n>We propose the Robust Autoencoder-based Unsupervised Feature Selection (RAEUFS) model, which leverages a deep autoencoder to learn nonlinear feature representations.<n>Our method outperforms state-of-the-art UFS approaches in both clean and outlier-contaminated data settings.
  arXiv  Detail & Related papers  (2025-12-21T12:42:37Z)
• CART-based Synthetic Tabular Data Generation for Imbalanced Regression [1.342834401139078]
  We propose adapting an existing CART-based synthetic data generation method, tailoring it for imbalanced regression.<n>The new method integrates relevance and density-based mechanisms to guide sampling in sparse regions of the target space.<n>Our experimental study focuses on the prediction of extreme target values across benchmark datasets.
  arXiv  Detail & Related papers  (2025-06-03T12:42:20Z)
• A Hybrid Framework for Statistical Feature Selection and Image-Based Noise-Defect Detection [55.2480439325792]
  This paper presents a hybrid framework that integrates both statistical feature selection and classification techniques to improve defect detection accuracy.<n>We present around 55 distinguished features that are extracted from industrial images, which are then analyzed using statistical methods.<n>By integrating these methods with flexible machine learning applications, the proposed framework improves detection accuracy and reduces false positives and misclassifications.
  arXiv  Detail & Related papers  (2024-12-11T22:12:21Z)
• Stochastic Gradient Descent with Adaptive Data [4.119418481809095]
  gradient descent (SGD) is a powerful optimization technique that is particularly useful in online learning scenarios. Applying SGD to policy optimization problems in operations research involves a distinct challenge: the policy changes the environment and thereby affects the data used to update the policy. The influence of previous decisions on the data generated introduces bias in the gradient estimate, which presents a potential source of instability for online learning not present in the iid case. We show that the convergence speed of SGD with adaptive data is largely similar to the classical iid setting, as long as the mixing time of the policy-induced dynamics is factored in.
  arXiv  Detail & Related papers  (2024-10-02T02:58:32Z)
• Tackling Diverse Minorities in Imbalanced Classification [80.78227787608714]
  Imbalanced datasets are commonly observed in various real-world applications, presenting significant challenges in training classifiers. We propose generating synthetic samples iteratively by mixing data samples from both minority and majority classes. We demonstrate the effectiveness of our proposed framework through extensive experiments conducted on seven publicly available benchmark datasets.
  arXiv  Detail & Related papers  (2023-08-28T18:48:34Z)
• Large-scale Fully-Unsupervised Re-Identification [78.47108158030213]
  We propose two strategies to learn from large-scale unlabeled data. The first strategy performs a local neighborhood sampling to reduce the dataset size in each without violating neighborhood relationships. A second strategy leverages a novel Re-Ranking technique, which has a lower time upper bound complexity and reduces the memory complexity from O(n2) to O(kn) with k n.
  arXiv  Detail & Related papers  (2023-07-26T16:19:19Z)
• Boosting Differentiable Causal Discovery via Adaptive Sample Reweighting [62.23057729112182]
  Differentiable score-based causal discovery methods learn a directed acyclic graph from observational data. We propose a model-agnostic framework to boost causal discovery performance by dynamically learning the adaptive weights for the Reweighted Score function, ReScore.
  arXiv  Detail & Related papers  (2023-03-06T14:49:59Z)
• Robust Fitted-Q-Evaluation and Iteration under Sequentially Exogenous Unobserved Confounders [9.401989343015364]
  We study robust policy evaluation and policy optimization in the presence of sequentially-exogenous unobserved confounders.<n>We provide sample complexity bounds, insights, and show effectiveness both in simulations and on real-world longitudinal healthcare data of treating sepsis.
  arXiv  Detail & Related papers  (2023-02-01T18:40:53Z)
• Learning while Respecting Privacy and Robustness to Distributional Uncertainties and Adversarial Data [66.78671826743884]
  The distributionally robust optimization framework is considered for training a parametric model. The objective is to endow the trained model with robustness against adversarially manipulated input data. Proposed algorithms offer robustness with little overhead.
  arXiv  Detail & Related papers  (2020-07-07T18:25:25Z)
• Dynamic Federated Learning [57.14673504239551]
  Federated learning has emerged as an umbrella term for centralized coordination strategies in multi-agent environments. We consider a federated learning model where at every iteration, a random subset of available agents perform local updates based on their data. Under a non-stationary random walk model on the true minimizer for the aggregate optimization problem, we establish that the performance of the architecture is determined by three factors, namely, the data variability at each agent, the model variability across all agents, and a tracking term that is inversely proportional to the learning rate of the algorithm.
  arXiv  Detail & Related papers  (2020-02-20T15:00:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.