核心概念
StiefelGen proposes a novel approach to time series data augmentation by leveraging the Stiefel manifold, allowing for precise control over signal perturbations. The method offers flexibility in emphasizing noise or basis function deformations, tailored to specific data generation needs.
摘要
StiefelGen introduces a unique methodology for time series data augmentation, addressing limitations in existing approaches. By traversing geodesic paths on the Stiefel manifold, the method enables controlled signal perturbations for both data augmentation and outlier detection tasks. The framework's interpretability and customization options make it a valuable tool for various applications, including structural health monitoring.
Data sets like SteamGen and New York Taxi are used to demonstrate the effectiveness of StiefelGen in generating synthetic data. The method's ability to separate noise and basis-driven deformations allows for precise adjustment of signals, avoiding trivial or catastrophic changes. Applications in structural health monitoring showcase how StiefelGen can enhance robustness analysis and adversarial data generation tasks.
統計資料
Substantial data currently exists within these sub-fields.
For instance, in CV, common techniques include stretching, flipping, cropping.
More recently, there have been innovations seen in mix-up strategies for data augmentation.
Despite the recent success of the aforementioned approaches for synthetic data generation.
This lack of focus can be partially attributed to the difficulties within the inherent temporal dependency structures within the time series tasks.
引述
"StiefelGen introduces a novel approach to time series data augmentation by leveraging matrix differential geometry."
"The method offers flexibility in emphasizing noise or basis function deformations based on specific needs."